Research_Evil_Twin_Detection.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/kirula0626/Evil-Twin-Detection-using-Machine-Learning/blob/main/Research_Evil_Twin_Detection.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dyzmlqevfNRz"
      },
      "source": [
        "# 0. Mount to Drive"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KbvYB93LfL0i",
        "outputId": "57d6b8c0-6ae3-45c1-f842-dad1117c8899"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "\n",
        "drive.mount(\"/content/drive\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "k6qdieKX-nQ8"
      },
      "source": [
        "#1. Information Gathering"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "G-ZC4860AGFK"
      },
      "source": [
        "## 1.1 AWID Dataset (ATK-F-Trn) - Full Tranning packets of different attacks"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_LVuCUVi-ZHD",
        "outputId": "a4544f24-5aff-4290-e7ca-1da3dff5e431"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "/content/drive/MyDrive/Dataset AWID/Zips/AWID2/AWID2_Dataset/DATASET/AWID-ATK-F-Trn\n"
          ]
        }
      ],
      "source": [
        "cd /content/drive/MyDrive/Dataset AWID/Zips/AWID2/AWID2_Dataset/DATASET/AWID-ATK-F-Trn"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "l3mc-v3w_Ldt",
        "outputId": "7bdeb8c0-e0d4-4cb2-ed6f-17fedaac7917"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Unique values in file 1.csv: ['class']\n",
            "Unique values in file 2.csv: ['class', 'normal']\n",
            "Unique values in file 3.csv: ['normal']\n",
            "Unique values in file 4.csv: ['normal']\n",
            "Unique values in file 5.csv: ['normal']\n",
            "Unique values in file 6.csv: ['normal']\n",
            "Unique values in file 7.csv: ['normal']\n",
            "Unique values in file 8.csv: ['normal']\n",
            "Unique values in file 9.csv: ['normal']\n",
            "Unique values in file 10.csv: ['normal']\n",
            "Unique values in file 11.csv: ['normal']\n",
            "Unique values in file 12.csv: ['normal']\n",
            "Unique values in file 13.csv: ['normal']\n",
            "Unique values in file 14.csv: ['normal']\n",
            "Unique values in file 15.csv: ['normal']\n",
            "Unique values in file 16.csv: ['normal']\n",
            "Unique values in file 17.csv: ['normal']\n",
            "Unique values in file 18.csv: ['normal']\n",
            "Unique values in file 19.csv: ['normal']\n",
            "Unique values in file 20.csv: ['normal']\n",
            "Unique values in file 21.csv: ['deauthentication', 'normal']\n",
            "Unique values in file 22.csv: ['beacon', 'deauthentication', 'amok', 'normal', 'authentication_request']\n",
            "Unique values in file 23.csv: ['deauthentication', 'normal', 'probe_response']\n",
            "Unique values in file 24.csv: ['beacon', 'deauthentication', 'normal', 'probe_response', 'authentication_request']\n",
            "Unique values in file 25.csv: ['normal']\n",
            "Unique values in file 26.csv: ['normal']\n",
            "Unique values in file 27.csv: ['normal']\n",
            "Unique values in file 28.csv: ['normal']\n",
            "Unique values in file 29.csv: ['normal']\n",
            "Unique values in file 30.csv: ['normal']\n",
            "Unique values in file 31.csv: ['normal']\n",
            "Unique values in file 32.csv: ['normal']\n",
            "Unique values in file 33.csv: ['normal']\n",
            "Unique values in file 34.csv: ['normal']\n",
            "Unique values in file 35.csv: ['normal']\n",
            "Unique values in file 36.csv: ['normal']\n",
            "Unique values in file 37.csv: ['normal']\n",
            "Unique values in file 38.csv: ['normal']\n",
            "Unique values in file 39.csv: ['normal']\n",
            "Unique values in file 40.csv: ['normal']\n",
            "Unique values in file 41.csv: ['normal']\n",
            "Unique values in file 42.csv: ['normal']\n",
            "Unique values in file 43.csv: ['normal']\n",
            "Unique values in file 44.csv: ['normal']\n",
            "Unique values in file 45.csv: ['normal']\n",
            "Unique values in file 46.csv: ['fragmentation', 'normal', 'arp']\n",
            "Unique values in file 47.csv: ['normal', 'arp']\n",
            "Unique values in file 48.csv: ['normal', 'arp']\n",
            "Unique values in file 49.csv: ['normal']\n",
            "Unique values in file 50.csv: ['normal']\n",
            "Unique values in file 51.csv: ['normal']\n",
            "Unique values in file 52.csv: ['normal']\n",
            "Unique values in file 53.csv: ['normal']\n",
            "Unique values in file 54.csv: ['normal']\n",
            "Unique values in file 55.csv: ['normal']\n",
            "Unique values in file 56.csv: ['normal']\n",
            "Unique values in file 57.csv: ['normal']\n",
            "Unique values in file 58.csv: ['normal']\n",
            "Unique values in file 59.csv: ['normal']\n",
            "Unique values in file 60.csv: ['normal']\n",
            "Unique values in file 61.csv: ['normal']\n",
            "Unique values in file 62.csv: ['normal']\n",
            "Unique values in file 63.csv: ['normal']\n",
            "Unique values in file 64.csv: ['normal']\n",
            "Unique values in file 65.csv: ['normal']\n",
            "Unique values in file 66.csv: ['normal']\n",
            "Unique values in file 67.csv: ['normal']\n",
            "Unique values in file 68.csv: ['normal']\n",
            "Unique values in file 69.csv: ['normal']\n",
            "Unique values in file 70.csv: ['deauthentication', 'normal', 'cafe_latte']\n",
            "Unique values in file 71.csv: ['deauthentication', 'normal', 'cafe_latte']\n",
            "Unique values in file 72.csv: ['deauthentication', 'evil_twin', 'normal', 'cafe_latte']\n",
            "Unique values in file 73.csv: ['normal']\n",
            "Unique values in file 74.csv: ['normal']\n",
            "Unique values in file 75.csv: ['normal']\n",
            "Unique values in file 76.csv: ['normal']\n",
            "Unique values in file 77.csv: ['normal']\n",
            "Unique values in file 78.csv: ['normal']\n",
            "Unique values in file 79.csv: ['normal']\n",
            "Unique values in file 80.csv: ['normal']\n",
            "Unique values in file 81.csv: ['normal']\n",
            "Unique values in file 82.csv: ['normal']\n",
            "Unique values in file 83.csv: ['normal']\n",
            "Unique values in file 84.csv: ['normal']\n",
            "Unique values in file 85.csv: ['normal']\n",
            "Unique values in file 86.csv: ['normal']\n",
            "Unique values in file 87.csv: ['normal']\n",
            "Unique values in file 88.csv: ['normal']\n",
            "Unique values in file 89.csv: ['normal']\n",
            "Unique values in file 90.csv: ['normal']\n",
            "Unique values in file 91.csv: ['normal']\n",
            "Unique values in file 92.csv: ['normal']\n",
            "Unique values in file 93.csv: ['normal']\n",
            "Unique values in file 94.csv: ['deauthentication', 'normal', 'authentication_request', 'amok']\n",
            "Unique values in file 95.csv: ['fragmentation', 'beacon', 'normal', 'probe_response', 'authentication_request']\n",
            "Unique values in file 96.csv: ['deauthentication', 'evil_twin', 'normal', 'cafe_latte', 'arp']\n"
          ]
        }
      ],
      "source": [
        "import csv\n",
        "# Finds unique values in the last column\n",
        "for i in range(1, 97):\n",
        "    with open(f'{i}.csv', 'r') as file:\n",
        "        reader = csv.reader(file)\n",
        "        last_column = [row[-1] for row in reader]\n",
        "        unique_values = list(set(last_column))\n",
        "        print(f'Unique values in file {i}.csv: {unique_values}')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "v887YW6cEd_O"
      },
      "source": [
        "---\n",
        "***Evil Twin exist***\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "1.   ***72.csv file***\n",
        "2.   ***96.csv file***\n",
        "\n",
        "\n",
        "\n",
        "---"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Mh0PWpUTCJ7l",
        "outputId": "bea88c66-92ad-4873-b538-9c6c8d4e6573"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "cafe_latte: 504497\n",
            "deauthentication: 583\n",
            "evil_twin: 16292\n",
            "normal: 3125938\n"
          ]
        }
      ],
      "source": [
        "import csv\n",
        "# Counts how many times each unique value appears\n",
        "counts = {}\n",
        "with open('72.csv', 'r') as file:\n",
        "    reader = csv.reader(file)\n",
        "    header = next(reader)  # Skip the header row\n",
        "    for row in reader:\n",
        "        last_col = row[-1]\n",
        "        if last_col not in counts:\n",
        "            counts[last_col] = 0\n",
        "        counts[last_col] += 1\n",
        "\n",
        "unique_values = sorted(counts.keys())\n",
        "for value in unique_values:\n",
        "    print(f'{value}: {counts[value]}')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MFHVtjNsCJIF",
        "outputId": "61b00e78-3d57-4aab-f6cc-f268b6d67fa3"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "arp: 494429\n",
            "cafe_latte: 367761\n",
            "deauthentication: 742\n",
            "evil_twin: 7306\n",
            "normal: 1999049\n"
          ]
        }
      ],
      "source": [
        "import csv\n",
        "# Counts how many times each unique value appears\n",
        "counts = {}\n",
        "with open('96.csv', 'r') as file:\n",
        "    reader = csv.reader(file)\n",
        "    header = next(reader)  # Skip the header row\n",
        "    for row in reader:\n",
        "        last_col = row[-1]\n",
        "        if last_col not in counts:\n",
        "            counts[last_col] = 0\n",
        "        counts[last_col] += 1\n",
        "\n",
        "unique_values = sorted(counts.keys())\n",
        "for value in unique_values:\n",
        "    print(f'{value}: {counts[value]}')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9cAPtU8aIxng",
        "outputId": "095d231f-5481-4b01-b85d-0ee78373a557"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "<ipython-input-14-b432e7d1adb3>:3: DtypeWarning: Columns (60,61,74) have mixed types. Specify dtype option on import or set low_memory=False.\n",
            "  df = pd.read_csv('72.csv')\n"
          ]
        },
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              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-4e0c70e9-1426-498e-b938-9a03223e1e25\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-4e0c70e9-1426-498e-b938-9a03223e1e25')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-4e0c70e9-1426-498e-b938-9a03223e1e25 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "   frame.interface_id  frame.dlt  frame.offset_shift  frame.time_epoch  \\\n",
              "0                   0         23                 0.0      1.425096e+09   \n",
              "1                   0         23                 0.0      1.425096e+09   \n",
              "2                   0         23                 0.0      1.425096e+09   \n",
              "3                   0         23                 0.0      1.425096e+09   \n",
              "4                   0         23                 0.0      1.425096e+09   \n",
              "\n",
              "   frame.time_delta  frame.time_delta_displayed  frame.time_relative  \\\n",
              "0          0.000000                    0.000000             0.000000   \n",
              "1          0.001473                    0.001473             0.001473   \n",
              "2          0.001014                    0.001014             0.002487   \n",
              "3          0.000030                    0.000030             0.002517   \n",
              "4          0.000010                    0.000010             0.002527   \n",
              "\n",
              "   frame.len  frame.cap_len  frame.marked  ...  wlan.qos.priority  \\\n",
              "0        110            110             0  ...                  ?   \n",
              "1        160            160             0  ...                  ?   \n",
              "2         58             58             0  ...                  ?   \n",
              "3         52             52             0  ...                  ?   \n",
              "4         70             70             0  ...                  ?   \n",
              "\n",
              "   wlan.qos.eosp  wlan.qos.ack  wlan.qos.amsdupresent  \\\n",
              "0              ?             ?                      ?   \n",
              "1              ?             ?                      ?   \n",
              "2              ?             ?                      ?   \n",
              "3              ?             ?                      ?   \n",
              "4              ?             ?                      ?   \n",
              "\n",
              "   wlan.qos.buf_state_indicated  wlan.qos.bit4  wlan.qos.txop_dur_req  \\\n",
              "0                             ?              ?                      ?   \n",
              "1                             ?              ?                      ?   \n",
              "2                             ?              ?                      ?   \n",
              "3                             ?              ?                      ?   \n",
              "4                             ?              ?                      ?   \n",
              "\n",
              "   wlan.qos.buf_state_indicated.1  data.len       class  \n",
              "0                               ?        36  cafe_latte  \n",
              "1                               ?         ?      normal  \n",
              "2                               ?         ?      normal  \n",
              "3                               ?         ?      normal  \n",
              "4                               ?         ?      normal  \n",
              "\n",
              "[5 rows x 155 columns]"
            ]
          },
          "execution_count": 14,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "import pandas as pd\n",
        "\n",
        "df = pd.read_csv('72.csv')\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VcbYGj2NIzBD",
        "outputId": "5ad04daf-6a00-4b9c-869a-4dec5dc7a95f"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "<ipython-input-15-91906bf0e7a5>:3: DtypeWarning: Columns (60,74) have mixed types. Specify dtype option on import or set low_memory=False.\n",
            "  df = pd.read_csv('96.csv')\n"
          ]
        },
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df"
            },
            "text/html": [
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              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "      <th></th>\n",
              "      <th>frame.interface_id</th>\n",
              "      <th>frame.dlt</th>\n",
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              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>52</td>\n",
              "      <td>52</td>\n",
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              "      <td>...</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
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              "      <td>1.425182e+09</td>\n",
              "      <td>0.000003</td>\n",
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              "      <td>70</td>\n",
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              "      <td>0</td>\n",
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              "      <td>?</td>\n",
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              "      <td>?</td>\n",
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              "      <td>0.000009</td>\n",
              "      <td>0.000009</td>\n",
              "      <td>0.000012</td>\n",
              "      <td>66</td>\n",
              "      <td>66</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
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              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
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              "      <td>0.000350</td>\n",
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              "      <td>?</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 155 columns</p>\n",
              "</div>\n",
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              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-af68a257-59f9-4a20-8a51-23414414f86c')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-af68a257-59f9-4a20-8a51-23414414f86c button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-af68a257-59f9-4a20-8a51-23414414f86c');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "    </g>\n",
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              "  </button>\n",
              "\n",
              "<style>\n",
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              "      --bg-color: #E8F0FE;\n",
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              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
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              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
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              "    display: none;\n",
              "    fill: var(--fill-color);\n",
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              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-ddf2f555-e08a-46be-a3a1-3306077c21b8 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "   frame.interface_id frame.dlt  frame.offset_shift  frame.time_epoch  \\\n",
              "0                   0         ?                 0.0      1.425182e+09   \n",
              "1                   0         ?                 0.0      1.425182e+09   \n",
              "2                   0         ?                 0.0      1.425182e+09   \n",
              "3                   0         ?                 0.0      1.425182e+09   \n",
              "4                   0         ?                 0.0      1.425182e+09   \n",
              "\n",
              "   frame.time_delta  frame.time_delta_displayed  frame.time_relative  \\\n",
              "0          0.000000                    0.000000             0.000000   \n",
              "1          0.000003                    0.000003             0.000003   \n",
              "2          0.000009                    0.000009             0.000012   \n",
              "3          0.000325                    0.000325             0.000337   \n",
              "4          0.000013                    0.000013             0.000350   \n",
              "\n",
              "   frame.len  frame.cap_len  frame.marked  ...  wlan.qos.priority  \\\n",
              "0         52             52             0  ...                  ?   \n",
              "1         70             70             0  ...                  ?   \n",
              "2         66             66             0  ...                  ?   \n",
              "3         52             52             0  ...                  ?   \n",
              "4         52             52             0  ...                  ?   \n",
              "\n",
              "   wlan.qos.eosp  wlan.qos.ack  wlan.qos.amsdupresent  \\\n",
              "0              ?             ?                      ?   \n",
              "1              ?             ?                      ?   \n",
              "2              ?             ?                      ?   \n",
              "3              ?             ?                      ?   \n",
              "4              ?             ?                      ?   \n",
              "\n",
              "   wlan.qos.buf_state_indicated  wlan.qos.bit4  wlan.qos.txop_dur_req  \\\n",
              "0                             ?              ?                      ?   \n",
              "1                             ?              ?                      ?   \n",
              "2                             ?              ?                      ?   \n",
              "3                             ?              ?                      ?   \n",
              "4                             ?              ?                      ?   \n",
              "\n",
              "   wlan.qos.buf_state_indicated.1  data.len   class  \n",
              "0                               ?         ?  normal  \n",
              "1                               ?         ?  normal  \n",
              "2                               ?         ?  normal  \n",
              "3                               ?         ?  normal  \n",
              "4                               ?         ?  normal  \n",
              "\n",
              "[5 rows x 155 columns]"
            ]
          },
          "execution_count": 15,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "import pandas as pd\n",
        "\n",
        "df = pd.read_csv('96.csv')\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "iC4bCr_aKtlP"
      },
      "outputs": [],
      "source": [
        "import csv\n",
        "import shutil\n",
        "import os\n",
        "\n",
        "# Features list\n",
        "features = [\n",
        "    'frame.interface_id',\n",
        "    'frame.dlt',\n",
        "    'frame.offset_shift',\n",
        "    'frame.time_epoch',\n",
        "    'frame.time_delta',\n",
        "    'frame.time_delta_displayed',\n",
        "    'frame.time_relative',\n",
        "    'frame.len',\n",
        "    'frame.cap_len',\n",
        "    'frame.marked',\n",
        "    'frame.ignored',\n",
        "    'radiotap.version',\n",
        "    'radiotap.pad',\n",
        "    'radiotap.length',\n",
        "    'radiotap.present.tsft',\n",
        "    'radiotap.present.flags',\n",
        "    'radiotap.present.rate',\n",
        "    'radiotap.present.channel',\n",
        "    'radiotap.present.fhss',\n",
        "    'radiotap.present.dbm_antsignal',\n",
        "    'radiotap.present.dbm_antnoise',\n",
        "    'radiotap.present.lock_quality',\n",
        "    'radiotap.present.tx_attenuation',\n",
        "    'radiotap.present.db_tx_attenuation',\n",
        "    'radiotap.present.dbm_tx_power',\n",
        "    'radiotap.present.antenna',\n",
        "    'radiotap.present.db_antsignal',\n",
        "    'radiotap.present.db_antnoise',\n",
        "    'radiotap.present.rxflags',\n",
        "    'radiotap.present.xchannel',\n",
        "    'radiotap.present.mcs',\n",
        "    'radiotap.present.ampdu',\n",
        "    'radiotap.present.vht',\n",
        "    'radiotap.present.reserved',\n",
        "    'radiotap.present.rtap_ns',\n",
        "    'radiotap.present.vendor_ns',\n",
        "    'radiotap.present.ext',\n",
        "    'radiotap.mactime',\n",
        "    'radiotap.flags.cfp',\n",
        "    'radiotap.flags.preamble',\n",
        "    'radiotap.flags.wep',\n",
        "    'radiotap.flags.frag',\n",
        "    'radiotap.flags.fcs',\n",
        "    'radiotap.flags.datapad',\n",
        "    'radiotap.flags.badfcs',\n",
        "    'radiotap.flags.shortgi',\n",
        "    'radiotap.datarate',\n",
        "    'radiotap.channel.freq',\n",
        "    'radiotap.channel.type.turbo',\n",
        "    'radiotap.channel.type.cck',\n",
        "    'radiotap.channel.type.ofdm',\n",
        "    'radiotap.channel.type.2ghz',\n",
        "    'radiotap.channel.type.5ghz',\n",
        "    'radiotap.channel.type.passive',\n",
        "    'radiotap.channel.type.dynamic',\n",
        "    'radiotap.channel.type.gfsk',\n",
        "    'radiotap.channel.type.gsm',\n",
        "    'radiotap.channel.type.sturbo',\n",
        "    'radiotap.channel.type.half',\n",
        "    'radiotap.channel.type.quarter',\n",
        "    'radiotap.dbm_antsignal',\n",
        "    'radiotap.antenna',\n",
        "    'radiotap.rxflags.badplcp',\n",
        "    'wlan.fc.type_subtype',\n",
        "    'wlan.fc.version',\n",
        "    'wlan.fc.type',\n",
        "    'wlan.fc.subtype',\n",
        "    'wlan.fc.ds',\n",
        "    'wlan.fc.frag',\n",
        "    'wlan.fc.retry',\n",
        "    'wlan.fc.pwrmgt',\n",
        "    'wlan.fc.moredata',\n",
        "    'wlan.fc.protected',\n",
        "    'wlan.fc.order',\n",
        "    'wlan.duration',\n",
        "    'wlan.ra',\n",
        "    'wlan.da',\n",
        "    'wlan.ta',\n",
        "    'wlan.sa',\n",
        "    'wlan.bssid',\n",
        "    'wlan.frag',\n",
        "    'wlan.seq',\n",
        "    'wlan.bar.type',\n",
        "    'wlan.ba.control.ackpolicy',\n",
        "    'wlan.ba.control.multitid',\n",
        "    'wlan.ba.control.cbitmap',\n",
        "    'wlan.bar.compressed.tidinfo',\n",
        "    'wlan.ba.bm',\n",
        "    'wlan.fcs_good',\n",
        "    'wlan_mgt.fixed.capabilities.ess',\n",
        "    'wlan_mgt.fixed.capabilities.ibss',\n",
        "    'wlan_mgt.fixed.capabilities.cfpoll.ap',\n",
        "    'wlan_mgt.fixed.capabilities.privacy',\n",
        "    'wlan_mgt.fixed.capabilities.preamble',\n",
        "    'wlan_mgt.fixed.capabilities.pbcc',\n",
        "    'wlan_mgt.fixed.capabilities.agility',\n",
        "    'wlan_mgt.fixed.capabilities.spec_man',\n",
        "    'wlan_mgt.fixed.capabilities.short_slot_time',\n",
        "    'wlan_mgt.fixed.capabilities.apsd',\n",
        "    'wlan_mgt.fixed.capabilities.radio_measurement',\n",
        "    'wlan_mgt.fixed.capabilities.dsss_ofdm',\n",
        "    'wlan_mgt.fixed.capabilities.del_blk_ack',\n",
        "    'wlan_mgt.fixed.capabilities.imm_blk_ack',\n",
        "    'wlan_mgt.fixed.listen_ival',\n",
        "    'wlan_mgt.fixed.current_ap',\n",
        "    'wlan_mgt.fixed.status_code',\n",
        "    'wlan_mgt.fixed.timestamp',\n",
        "    'wlan_mgt.fixed.beacon',\n",
        "    'wlan_mgt.fixed.aid',\n",
        "    'wlan_mgt.fixed.reason_code',\n",
        "    'wlan_mgt.fixed.auth.alg',\n",
        "    'wlan_mgt.fixed.auth_seq',\n",
        "    'wlan_mgt.fixed.category_code',\n",
        "    'wlan_mgt.fixed.htact',\n",
        "    'wlan_mgt.fixed.chanwidth',\n",
        "    'wlan_mgt.fixed.fragment',\n",
        "    'wlan_mgt.fixed.sequence',\n",
        "    'wlan_mgt.tagged.all',\n",
        "    'wlan_mgt.ssid',\n",
        "    'wlan_mgt.ds.current_channel',\n",
        "    'wlan_mgt.tim.dtim_count',\n",
        "    'wlan_mgt.tim.dtim_period',\n",
        "    'wlan_mgt.tim.bmapctl.multicast',\n",
        "    'wlan_mgt.tim.bmapctl.offset',\n",
        "    'wlan_mgt.country_info.environment',\n",
        "    'wlan_mgt.rsn.version',\n",
        "    'wlan_mgt.rsn.gcs.type',\n",
        "    'wlan_mgt.rsn.pcs.count',\n",
        "    'wlan_mgt.rsn.akms.count',\n",
        "    'wlan_mgt.rsn.akms.type',\n",
        "    'wlan_mgt.rsn.capabilities.preauth',\n",
        "    'wlan_mgt.rsn.capabilities.no_pairwise',\n",
        "    'wlan_mgt.rsn.capabilities.ptksa_replay_counter',\n",
        "    'wlan_mgt.rsn.capabilities.gtksa_replay_counter',\n",
        "    'wlan_mgt.rsn.capabilities.mfpr',\n",
        "    'wlan_mgt.rsn.capabilities.mfpc',\n",
        "    'wlan_mgt.rsn.capabilities.peerkey',\n",
        "    'wlan_mgt.tcprep.trsmt_pow',\n",
        "    'wlan_mgt.tcprep.link_mrg',\n",
        "    'wlan.wep.iv',\n",
        "    'wlan.wep.key',\n",
        "    'wlan.wep.icv',\n",
        "    'wlan.tkip.extiv',\n",
        "    'wlan.ccmp.extiv',\n",
        "    'wlan.qos.tid',\n",
        "    'wlan.qos.priority',\n",
        "    'wlan.qos.eosp',\n",
        "    'wlan.qos.ack',\n",
        "    'wlan.qos.amsdupresent',\n",
        "    'wlan.qos.buf_state_indicated',\n",
        "    'wlan.qos.bit4',\n",
        "    'wlan.qos.txop_dur_req',\n",
        "    'wlan.qos.buf_state_indicated',\n",
        "    'data.len',\n",
        "    'class'\n",
        "]\n",
        "\n",
        "# List of specific CSV files to process\n",
        "csv_files_to_process = [72, 96]\n",
        "\n",
        "# Define the source and destination directory paths\n",
        "source_dir = '/content/drive/MyDrive/Dataset AWID/Zips/AWID2/AWID2_Dataset/DATASET/AWID-ATK-F-Trn'\n",
        "destination_dir = '/content/drive/MyDrive/Dataset AWID/Zips/AWID2/AWID2_Dataset/'\n",
        "\n",
        "# Loop through the specified CSV files\n",
        "for i in csv_files_to_process:\n",
        "    # Define the CSV file paths\n",
        "    existing_csv_file_path = os.path.join(source_dir, f'{i}.csv')\n",
        "    new_csv_file_path = os.path.join(destination_dir, f'{i}_new.csv')\n",
        "\n",
        "    # Read existing data from the CSV file\n",
        "    existing_data = []\n",
        "    with open(existing_csv_file_path, 'r', newline='') as csvfile:\n",
        "        reader = csv.reader(csvfile)\n",
        "        existing_data.extend(list(reader))\n",
        "\n",
        "    # Write the existing data along with the features as headers to the new CSV file\n",
        "    with open(new_csv_file_path, 'w', newline='') as csvfile:\n",
        "        writer = csv.writer(csvfile)\n",
        "        writer.writerow(features)  # Add headers (global variable)\n",
        "        writer.writerows(existing_data)  # Write the existing data\n",
        "\n",
        "    # Replace the existing CSV file with the new one\n",
        "    shutil.move(new_csv_file_path, existing_csv_file_path)\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 290
        },
        "id": "ws9vaDpcK-_J",
        "outputId": "f2de3aeb-74d9-4cf1-81df-5f9c93d98cc1"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "<ipython-input-11-72d7eeb146b6>:3: DtypeWarning: Columns (60,61,74) have mixed types. Specify dtype option on import or set low_memory=False.\n",
            "  df = pd.read_csv('/content/drive/MyDrive/Dataset AWID/Zips/AWID2/AWID2_Dataset/72.csv')\n"
          ]
        },
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df"
            },
            "text/html": [
              "\n",
              "  <div id=\"df-67c81256-2d71-4d71-855a-4d345739f4ad\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>frame.interface_id</th>\n",
              "      <th>frame.dlt</th>\n",
              "      <th>frame.offset_shift</th>\n",
              "      <th>frame.time_epoch</th>\n",
              "      <th>frame.time_delta</th>\n",
              "      <th>frame.time_delta_displayed</th>\n",
              "      <th>frame.time_relative</th>\n",
              "      <th>frame.len</th>\n",
              "      <th>frame.cap_len</th>\n",
              "      <th>frame.marked</th>\n",
              "      <th>...</th>\n",
              "      <th>wlan.qos.priority</th>\n",
              "      <th>wlan.qos.eosp</th>\n",
              "      <th>wlan.qos.ack</th>\n",
              "      <th>wlan.qos.amsdupresent</th>\n",
              "      <th>wlan.qos.buf_state_indicated</th>\n",
              "      <th>wlan.qos.bit4</th>\n",
              "      <th>wlan.qos.txop_dur_req</th>\n",
              "      <th>wlan.qos.buf_state_indicated.1</th>\n",
              "      <th>data.len</th>\n",
              "      <th>class</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>23</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.425096e+09</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>110</td>\n",
              "      <td>110</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>36</td>\n",
              "      <td>cafe_latte</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>23</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.425096e+09</td>\n",
              "      <td>0.001473</td>\n",
              "      <td>0.001473</td>\n",
              "      <td>0.001473</td>\n",
              "      <td>160</td>\n",
              "      <td>160</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>23</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.425096e+09</td>\n",
              "      <td>0.001014</td>\n",
              "      <td>0.001014</td>\n",
              "      <td>0.002487</td>\n",
              "      <td>58</td>\n",
              "      <td>58</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>23</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.425096e+09</td>\n",
              "      <td>0.000030</td>\n",
              "      <td>0.000030</td>\n",
              "      <td>0.002517</td>\n",
              "      <td>52</td>\n",
              "      <td>52</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>23</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.425096e+09</td>\n",
              "      <td>0.000010</td>\n",
              "      <td>0.000010</td>\n",
              "      <td>0.002527</td>\n",
              "      <td>70</td>\n",
              "      <td>70</td>\n",
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              "      <td>?</td>\n",
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              "\n",
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              "\n",
              "  @keyframes spin {\n",
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              "      border-color: transparent;\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "\n",
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            ],
            "text/plain": [
              "   frame.interface_id  frame.dlt  frame.offset_shift  frame.time_epoch  \\\n",
              "0                   0         23                 0.0      1.425096e+09   \n",
              "1                   0         23                 0.0      1.425096e+09   \n",
              "2                   0         23                 0.0      1.425096e+09   \n",
              "3                   0         23                 0.0      1.425096e+09   \n",
              "4                   0         23                 0.0      1.425096e+09   \n",
              "\n",
              "   frame.time_delta  frame.time_delta_displayed  frame.time_relative  \\\n",
              "0          0.000000                    0.000000             0.000000   \n",
              "1          0.001473                    0.001473             0.001473   \n",
              "2          0.001014                    0.001014             0.002487   \n",
              "3          0.000030                    0.000030             0.002517   \n",
              "4          0.000010                    0.000010             0.002527   \n",
              "\n",
              "   frame.len  frame.cap_len  frame.marked  ...  wlan.qos.priority  \\\n",
              "0        110            110             0  ...                  ?   \n",
              "1        160            160             0  ...                  ?   \n",
              "2         58             58             0  ...                  ?   \n",
              "3         52             52             0  ...                  ?   \n",
              "4         70             70             0  ...                  ?   \n",
              "\n",
              "   wlan.qos.eosp  wlan.qos.ack  wlan.qos.amsdupresent  \\\n",
              "0              ?             ?                      ?   \n",
              "1              ?             ?                      ?   \n",
              "2              ?             ?                      ?   \n",
              "3              ?             ?                      ?   \n",
              "4              ?             ?                      ?   \n",
              "\n",
              "   wlan.qos.buf_state_indicated  wlan.qos.bit4  wlan.qos.txop_dur_req  \\\n",
              "0                             ?              ?                      ?   \n",
              "1                             ?              ?                      ?   \n",
              "2                             ?              ?                      ?   \n",
              "3                             ?              ?                      ?   \n",
              "4                             ?              ?                      ?   \n",
              "\n",
              "   wlan.qos.buf_state_indicated.1  data.len       class  \n",
              "0                               ?        36  cafe_latte  \n",
              "1                               ?         ?      normal  \n",
              "2                               ?         ?      normal  \n",
              "3                               ?         ?      normal  \n",
              "4                               ?         ?      normal  \n",
              "\n",
              "[5 rows x 155 columns]"
            ]
          },
          "execution_count": 11,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "import pandas as pd\n",
        "\n",
        "df = pd.read_csv('/content/drive/MyDrive/Dataset AWID/Zips/AWID2/AWID2_Dataset/72.csv')\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 290
        },
        "id": "kQBwChjgK_j_",
        "outputId": "281dc03a-3e59-49b6-fe41-20e4ee98e027"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "<ipython-input-12-ede88141856a>:3: DtypeWarning: Columns (60,74) have mixed types. Specify dtype option on import or set low_memory=False.\n",
            "  df = pd.read_csv('/content/drive/MyDrive/Dataset AWID/Zips/AWID2/AWID2_Dataset/96.csv')\n"
          ]
        },
        {
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              "variable_name": "df"
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              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
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              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-9ba70480-9618-4b71-88a1-04e2e079cf65 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-9ba70480-9618-4b71-88a1-04e2e079cf65');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "\n",
              "\n",
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              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-27e89745-7f01-4130-9e28-d70ea8cc8ba9')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-27e89745-7f01-4130-9e28-d70ea8cc8ba9 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "   frame.interface_id frame.dlt  frame.offset_shift  frame.time_epoch  \\\n",
              "0                   0         ?                 0.0      1.425182e+09   \n",
              "1                   0         ?                 0.0      1.425182e+09   \n",
              "2                   0         ?                 0.0      1.425182e+09   \n",
              "3                   0         ?                 0.0      1.425182e+09   \n",
              "4                   0         ?                 0.0      1.425182e+09   \n",
              "\n",
              "   frame.time_delta  frame.time_delta_displayed  frame.time_relative  \\\n",
              "0          0.000000                    0.000000             0.000000   \n",
              "1          0.000003                    0.000003             0.000003   \n",
              "2          0.000009                    0.000009             0.000012   \n",
              "3          0.000325                    0.000325             0.000337   \n",
              "4          0.000013                    0.000013             0.000350   \n",
              "\n",
              "   frame.len  frame.cap_len  frame.marked  ...  wlan.qos.priority  \\\n",
              "0         52             52             0  ...                  ?   \n",
              "1         70             70             0  ...                  ?   \n",
              "2         66             66             0  ...                  ?   \n",
              "3         52             52             0  ...                  ?   \n",
              "4         52             52             0  ...                  ?   \n",
              "\n",
              "   wlan.qos.eosp  wlan.qos.ack  wlan.qos.amsdupresent  \\\n",
              "0              ?             ?                      ?   \n",
              "1              ?             ?                      ?   \n",
              "2              ?             ?                      ?   \n",
              "3              ?             ?                      ?   \n",
              "4              ?             ?                      ?   \n",
              "\n",
              "   wlan.qos.buf_state_indicated  wlan.qos.bit4  wlan.qos.txop_dur_req  \\\n",
              "0                             ?              ?                      ?   \n",
              "1                             ?              ?                      ?   \n",
              "2                             ?              ?                      ?   \n",
              "3                             ?              ?                      ?   \n",
              "4                             ?              ?                      ?   \n",
              "\n",
              "   wlan.qos.buf_state_indicated.1  data.len   class  \n",
              "0                               ?         ?  normal  \n",
              "1                               ?         ?  normal  \n",
              "2                               ?         ?  normal  \n",
              "3                               ?         ?  normal  \n",
              "4                               ?         ?  normal  \n",
              "\n",
              "[5 rows x 155 columns]"
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "import pandas as pd\n",
        "\n",
        "df = pd.read_csv('/content/drive/MyDrive/Dataset AWID/Zips/AWID2/AWID2_Dataset/96.csv')\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aKjpPBBG1wFm"
      },
      "source": [
        "## 1.2 AWID Dataset (ATK-F-Tst) - Full Testing packets of different attacks"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aQOX06GO2Aud",
        "outputId": "e42d0c35-285d-4dd2-bd30-68a81f487edf"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "/content/drive/MyDrive/Dataset AWID/Zips/AWID2/AWID2_Dataset/DATASET/AWID-ATK-F-Tst\n"
          ]
        }
      ],
      "source": [
        "cd /content/drive/MyDrive/Dataset AWID/Zips/AWID2/AWID2_Dataset/DATASET/AWID-ATK-F-Tst"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YjP1C5n22I1J",
        "outputId": "4229729a-43c7-45ed-e6cf-c4f3f154c55c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Unique values in file 1.csv: ['normal']\n",
            "Unique values in file 2.csv: ['normal']\n",
            "Unique values in file 3.csv: ['normal']\n",
            "Unique values in file 4.csv: ['normal']\n",
            "Unique values in file 5.csv: ['normal']\n",
            "Unique values in file 6.csv: ['normal']\n",
            "Unique values in file 7.csv: ['arp', 'normal', 'fragmentation', 'chop_chop']\n",
            "Unique values in file 8.csv: ['arp', 'deauthentication', 'cafe_latte', 'fragmentation', 'normal', 'hirte']\n",
            "Unique values in file 9.csv: ['normal', 'deauthentication', 'hirte', 'class', 'evil_twin']\n",
            "Unique values in file 10.csv: ['deauthentication', 'probe_request', 'normal', 'class', 'evil_twin']\n",
            "Unique values in file 11.csv: ['deauthentication', 'probe_request', 'authentication_request', 'cts', 'beacon', 'disassociation', 'rts', 'normal']\n",
            "Unique values in file 12.csv: ['authentication_request', 'power', 'disassociation', 'probe_response', 'amok', 'normal']\n"
          ]
        }
      ],
      "source": [
        "import csv\n",
        "\n",
        "for i in range(1, 13):\n",
        "    with open(f'{i}.csv', 'r') as file:\n",
        "        reader = csv.reader(file)\n",
        "        last_column = [row[-1] for row in reader]\n",
        "        unique_values = list(set(last_column))\n",
        "        print(f'Unique values in file {i}.csv: {unique_values}')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8jXIL5l13ds1"
      },
      "source": [
        "---\n",
        "***Evil Twin exist***\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "1.   ***9.csv file***\n",
        "2.   ***10.csv file***\n",
        "\n",
        "\n",
        "\n",
        "---"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jhtgB3Of3dMx",
        "outputId": "17ef0250-1000-4fb1-8390-a0286517925c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "deauthentication: 1016\n",
            "evil_twin: 11238\n",
            "hirte: 391640\n",
            "normal: 4124709\n"
          ]
        }
      ],
      "source": [
        "import csv\n",
        "# Counts how many times each unique value appears\n",
        "counts = {}\n",
        "with open('9.csv', 'r') as file:\n",
        "    reader = csv.reader(file)\n",
        "    header = next(reader)  # Skip the header row\n",
        "    for row in reader:\n",
        "        last_col = row[-1]\n",
        "        if last_col not in counts:\n",
        "            counts[last_col] = 0\n",
        "        counts[last_col] += 1\n",
        "\n",
        "unique_values = sorted(counts.keys())\n",
        "for value in unique_values:\n",
        "    print(f'{value}: {counts[value]}')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fxui6Wcg3zY5",
        "outputId": "1265535e-3c75-46fa-c0a4-49c6040ed422"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "deauthentication: 941\n",
            "evil_twin: 15807\n",
            "normal: 3748435\n",
            "probe_request: 6237\n"
          ]
        }
      ],
      "source": [
        "import csv\n",
        "# Counts how many times each unique value appears\n",
        "counts = {}\n",
        "with open('10.csv', 'r') as file:\n",
        "    reader = csv.reader(file)\n",
        "    header = next(reader)  # Skip the header row\n",
        "    for row in reader:\n",
        "        last_col = row[-1]\n",
        "        if last_col not in counts:\n",
        "            counts[last_col] = 0\n",
        "        counts[last_col] += 1\n",
        "\n",
        "unique_values = sorted(counts.keys())\n",
        "for value in unique_values:\n",
        "    print(f'{value}: {counts[value]}')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 290
        },
        "id": "bKaRxxoS4RUA",
        "outputId": "79101dc8-c7f3-4c3b-8bd8-4ee383003dc9"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "<ipython-input-23-99a200f661b1>:3: DtypeWarning: Columns (60) have mixed types. Specify dtype option on import or set low_memory=False.\n",
            "  df = pd.read_csv('9.csv')\n"
          ]
        },
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df"
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              "\n",
              "  <div id=\"df-60809ac7-1a94-40fe-99bc-f8a3fa72ad8b\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
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              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "      <th>?</th>\n",
              "      <th>0.000000000</th>\n",
              "      <th>1425262336.098695000</th>\n",
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              "      <th>0</th>\n",
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              "      <td>normal</td>\n",
              "    </tr>\n",
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              "      <th>1</th>\n",
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              "      <td>?</td>\n",
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              "      <td>normal</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>?</td>\n",
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              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
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              "      <td>36</td>\n",
              "      <td>normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
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              "\n",
              "[5 rows x 155 columns]"
            ]
          },
          "execution_count": 23,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "import pandas as pd\n",
        "\n",
        "df = pd.read_csv('9.csv')\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 290
        },
        "id": "_nZXGFAN4RzQ",
        "outputId": "4fe464bd-879a-4e01-bdf3-dbe07c8d8552"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "<ipython-input-24-ee09d34551be>:3: DtypeWarning: Columns (60,74,88) have mixed types. Specify dtype option on import or set low_memory=False.\n",
            "  df = pd.read_csv('10.csv')\n"
          ]
        },
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              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-952db43f-49aa-455f-b220-8d9187dfa9c3');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-c42181f3-6609-4a62-a921-bd4275bc77cd\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-c42181f3-6609-4a62-a921-bd4275bc77cd')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-c42181f3-6609-4a62-a921-bd4275bc77cd button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "   0  ?  0.000000000  1425265936.199540000  0.000000000.1  0.000000000.2  \\\n",
              "0  0  ?          0.0          1.425266e+09       0.000005       0.000005   \n",
              "1  0  ?          0.0          1.425266e+09       0.000003       0.000003   \n",
              "2  0  ?          0.0          1.425266e+09       0.000004       0.000004   \n",
              "3  0  ?          0.0          1.425266e+09       0.000003       0.000003   \n",
              "4  0  ?          0.0          1.425266e+09       0.000004       0.000004   \n",
              "\n",
              "   0.000000000.3  68  68.1  0.1  ...  0.47  ?.62  0x0000  ?.63  ?.64  0.48  \\\n",
              "0       0.000005  52    52    0  ...     ?     ?       ?     ?     ?     ?   \n",
              "1       0.000008  66    66    0  ...     ?     ?       ?     ?     ?     ?   \n",
              "2       0.000012  52    52    0  ...     ?     ?       ?     ?     ?     ?   \n",
              "3       0.000015  68    68    0  ...     0     ?  0x0000     ?     ?     0   \n",
              "4       0.000019  52    52    0  ...     ?     ?       ?     ?     ?     ?   \n",
              "\n",
              "   0.49  ?.65  ?.66  normal  \n",
              "0     ?     ?     ?  normal  \n",
              "1     ?     ?     ?  normal  \n",
              "2     ?     ?     ?  normal  \n",
              "3     0     ?     ?  normal  \n",
              "4     ?     ?     ?  normal  \n",
              "\n",
              "[5 rows x 155 columns]"
            ]
          },
          "execution_count": 24,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "import pandas as pd\n",
        "\n",
        "df = pd.read_csv('10.csv')\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "XQVdR5ql4eH_"
      },
      "outputs": [],
      "source": [
        "import csv\n",
        "import shutil\n",
        "import os\n",
        "\n",
        "# Features list\n",
        "features = [\n",
        "    'frame.interface_id',\n",
        "    'frame.dlt',\n",
        "    'frame.offset_shift',\n",
        "    'frame.time_epoch',\n",
        "    'frame.time_delta',\n",
        "    'frame.time_delta_displayed',\n",
        "    'frame.time_relative',\n",
        "    'frame.len',\n",
        "    'frame.cap_len',\n",
        "    'frame.marked',\n",
        "    'frame.ignored',\n",
        "    'radiotap.version',\n",
        "    'radiotap.pad',\n",
        "    'radiotap.length',\n",
        "    'radiotap.present.tsft',\n",
        "    'radiotap.present.flags',\n",
        "    'radiotap.present.rate',\n",
        "    'radiotap.present.channel',\n",
        "    'radiotap.present.fhss',\n",
        "    'radiotap.present.dbm_antsignal',\n",
        "    'radiotap.present.dbm_antnoise',\n",
        "    'radiotap.present.lock_quality',\n",
        "    'radiotap.present.tx_attenuation',\n",
        "    'radiotap.present.db_tx_attenuation',\n",
        "    'radiotap.present.dbm_tx_power',\n",
        "    'radiotap.present.antenna',\n",
        "    'radiotap.present.db_antsignal',\n",
        "    'radiotap.present.db_antnoise',\n",
        "    'radiotap.present.rxflags',\n",
        "    'radiotap.present.xchannel',\n",
        "    'radiotap.present.mcs',\n",
        "    'radiotap.present.ampdu',\n",
        "    'radiotap.present.vht',\n",
        "    'radiotap.present.reserved',\n",
        "    'radiotap.present.rtap_ns',\n",
        "    'radiotap.present.vendor_ns',\n",
        "    'radiotap.present.ext',\n",
        "    'radiotap.mactime',\n",
        "    'radiotap.flags.cfp',\n",
        "    'radiotap.flags.preamble',\n",
        "    'radiotap.flags.wep',\n",
        "    'radiotap.flags.frag',\n",
        "    'radiotap.flags.fcs',\n",
        "    'radiotap.flags.datapad',\n",
        "    'radiotap.flags.badfcs',\n",
        "    'radiotap.flags.shortgi',\n",
        "    'radiotap.datarate',\n",
        "    'radiotap.channel.freq',\n",
        "    'radiotap.channel.type.turbo',\n",
        "    'radiotap.channel.type.cck',\n",
        "    'radiotap.channel.type.ofdm',\n",
        "    'radiotap.channel.type.2ghz',\n",
        "    'radiotap.channel.type.5ghz',\n",
        "    'radiotap.channel.type.passive',\n",
        "    'radiotap.channel.type.dynamic',\n",
        "    'radiotap.channel.type.gfsk',\n",
        "    'radiotap.channel.type.gsm',\n",
        "    'radiotap.channel.type.sturbo',\n",
        "    'radiotap.channel.type.half',\n",
        "    'radiotap.channel.type.quarter',\n",
        "    'radiotap.dbm_antsignal',\n",
        "    'radiotap.antenna',\n",
        "    'radiotap.rxflags.badplcp',\n",
        "    'wlan.fc.type_subtype',\n",
        "    'wlan.fc.version',\n",
        "    'wlan.fc.type',\n",
        "    'wlan.fc.subtype',\n",
        "    'wlan.fc.ds',\n",
        "    'wlan.fc.frag',\n",
        "    'wlan.fc.retry',\n",
        "    'wlan.fc.pwrmgt',\n",
        "    'wlan.fc.moredata',\n",
        "    'wlan.fc.protected',\n",
        "    'wlan.fc.order',\n",
        "    'wlan.duration',\n",
        "    'wlan.ra',\n",
        "    'wlan.da',\n",
        "    'wlan.ta',\n",
        "    'wlan.sa',\n",
        "    'wlan.bssid',\n",
        "    'wlan.frag',\n",
        "    'wlan.seq',\n",
        "    'wlan.bar.type',\n",
        "    'wlan.ba.control.ackpolicy',\n",
        "    'wlan.ba.control.multitid',\n",
        "    'wlan.ba.control.cbitmap',\n",
        "    'wlan.bar.compressed.tidinfo',\n",
        "    'wlan.ba.bm',\n",
        "    'wlan.fcs_good',\n",
        "    'wlan_mgt.fixed.capabilities.ess',\n",
        "    'wlan_mgt.fixed.capabilities.ibss',\n",
        "    'wlan_mgt.fixed.capabilities.cfpoll.ap',\n",
        "    'wlan_mgt.fixed.capabilities.privacy',\n",
        "    'wlan_mgt.fixed.capabilities.preamble',\n",
        "    'wlan_mgt.fixed.capabilities.pbcc',\n",
        "    'wlan_mgt.fixed.capabilities.agility',\n",
        "    'wlan_mgt.fixed.capabilities.spec_man',\n",
        "    'wlan_mgt.fixed.capabilities.short_slot_time',\n",
        "    'wlan_mgt.fixed.capabilities.apsd',\n",
        "    'wlan_mgt.fixed.capabilities.radio_measurement',\n",
        "    'wlan_mgt.fixed.capabilities.dsss_ofdm',\n",
        "    'wlan_mgt.fixed.capabilities.del_blk_ack',\n",
        "    'wlan_mgt.fixed.capabilities.imm_blk_ack',\n",
        "    'wlan_mgt.fixed.listen_ival',\n",
        "    'wlan_mgt.fixed.current_ap',\n",
        "    'wlan_mgt.fixed.status_code',\n",
        "    'wlan_mgt.fixed.timestamp',\n",
        "    'wlan_mgt.fixed.beacon',\n",
        "    'wlan_mgt.fixed.aid',\n",
        "    'wlan_mgt.fixed.reason_code',\n",
        "    'wlan_mgt.fixed.auth.alg',\n",
        "    'wlan_mgt.fixed.auth_seq',\n",
        "    'wlan_mgt.fixed.category_code',\n",
        "    'wlan_mgt.fixed.htact',\n",
        "    'wlan_mgt.fixed.chanwidth',\n",
        "    'wlan_mgt.fixed.fragment',\n",
        "    'wlan_mgt.fixed.sequence',\n",
        "    'wlan_mgt.tagged.all',\n",
        "    'wlan_mgt.ssid',\n",
        "    'wlan_mgt.ds.current_channel',\n",
        "    'wlan_mgt.tim.dtim_count',\n",
        "    'wlan_mgt.tim.dtim_period',\n",
        "    'wlan_mgt.tim.bmapctl.multicast',\n",
        "    'wlan_mgt.tim.bmapctl.offset',\n",
        "    'wlan_mgt.country_info.environment',\n",
        "    'wlan_mgt.rsn.version',\n",
        "    'wlan_mgt.rsn.gcs.type',\n",
        "    'wlan_mgt.rsn.pcs.count',\n",
        "    'wlan_mgt.rsn.akms.count',\n",
        "    'wlan_mgt.rsn.akms.type',\n",
        "    'wlan_mgt.rsn.capabilities.preauth',\n",
        "    'wlan_mgt.rsn.capabilities.no_pairwise',\n",
        "    'wlan_mgt.rsn.capabilities.ptksa_replay_counter',\n",
        "    'wlan_mgt.rsn.capabilities.gtksa_replay_counter',\n",
        "    'wlan_mgt.rsn.capabilities.mfpr',\n",
        "    'wlan_mgt.rsn.capabilities.mfpc',\n",
        "    'wlan_mgt.rsn.capabilities.peerkey',\n",
        "    'wlan_mgt.tcprep.trsmt_pow',\n",
        "    'wlan_mgt.tcprep.link_mrg',\n",
        "    'wlan.wep.iv',\n",
        "    'wlan.wep.key',\n",
        "    'wlan.wep.icv',\n",
        "    'wlan.tkip.extiv',\n",
        "    'wlan.ccmp.extiv',\n",
        "    'wlan.qos.tid',\n",
        "    'wlan.qos.priority',\n",
        "    'wlan.qos.eosp',\n",
        "    'wlan.qos.ack',\n",
        "    'wlan.qos.amsdupresent',\n",
        "    'wlan.qos.buf_state_indicated',\n",
        "    'wlan.qos.bit4',\n",
        "    'wlan.qos.txop_dur_req',\n",
        "    'wlan.qos.buf_state_indicated',\n",
        "    'data.len',\n",
        "    'class'\n",
        "]\n",
        "\n",
        "# List of specific CSV files to process\n",
        "csv_files_to_process = [9, 10]\n",
        "\n",
        "# Define the source and destination directory paths\n",
        "source_dir = '/content/drive/MyDrive/Dataset AWID/Zips/AWID2/AWID2_Dataset/DATASET/AWID-ATK-F-Tst'\n",
        "destination_dir = '/content/drive/MyDrive/Dataset AWID/Zips/AWID2/AWID2_Dataset/'\n",
        "\n",
        "# Loop through the specified CSV files\n",
        "for i in csv_files_to_process:\n",
        "    # Define the CSV file paths\n",
        "    existing_csv_file_path = os.path.join(source_dir, f'{i}.csv')\n",
        "    new_csv_file_path = os.path.join(destination_dir, f'{i}_new.csv')\n",
        "\n",
        "    # Read existing data from the CSV file\n",
        "    existing_data = []\n",
        "    with open(existing_csv_file_path, 'r', newline='') as csvfile:\n",
        "        reader = csv.reader(csvfile)\n",
        "        existing_data.extend(list(reader))\n",
        "\n",
        "    # Write the existing data along with the features as headers to the new CSV file\n",
        "    with open(new_csv_file_path, 'w', newline='') as csvfile:\n",
        "        writer = csv.writer(csvfile)\n",
        "        writer.writerow(features)  # Add headers (global variable)\n",
        "        writer.writerows(existing_data)  # Write the existing data\n",
        "\n",
        "    # Replace the existing CSV file with the new one\n",
        "    shutil.move(new_csv_file_path, existing_csv_file_path)\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 290
        },
        "id": "u15z5UOlGuJd",
        "outputId": "091bae89-78e1-45f3-cad9-9b95cfbc5743"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "<ipython-input-27-ee09d34551be>:3: DtypeWarning: Columns (60,74,88) have mixed types. Specify dtype option on import or set low_memory=False.\n",
            "  df = pd.read_csv('10.csv')\n"
          ]
        },
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df"
            },
            "text/html": [
              "\n",
              "  <div id=\"df-8981df8c-7241-4e93-81e3-99c1f225320a\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>frame.interface_id</th>\n",
              "      <th>frame.dlt</th>\n",
              "      <th>frame.offset_shift</th>\n",
              "      <th>frame.time_epoch</th>\n",
              "      <th>frame.time_delta</th>\n",
              "      <th>frame.time_delta_displayed</th>\n",
              "      <th>frame.time_relative</th>\n",
              "      <th>frame.len</th>\n",
              "      <th>frame.cap_len</th>\n",
              "      <th>frame.marked</th>\n",
              "      <th>...</th>\n",
              "      <th>wlan.qos.priority</th>\n",
              "      <th>wlan.qos.eosp</th>\n",
              "      <th>wlan.qos.ack</th>\n",
              "      <th>wlan.qos.amsdupresent</th>\n",
              "      <th>wlan.qos.buf_state_indicated</th>\n",
              "      <th>wlan.qos.bit4</th>\n",
              "      <th>wlan.qos.txop_dur_req</th>\n",
              "      <th>wlan.qos.buf_state_indicated.1</th>\n",
              "      <th>data.len</th>\n",
              "      <th>class</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>?</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.425266e+09</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>68</td>\n",
              "      <td>68</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>?</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.425266e+09</td>\n",
              "      <td>0.000005</td>\n",
              "      <td>0.000005</td>\n",
              "      <td>0.000005</td>\n",
              "      <td>52</td>\n",
              "      <td>52</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
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              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>normal</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
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              "      <td>1.425266e+09</td>\n",
              "      <td>0.000003</td>\n",
              "      <td>0.000003</td>\n",
              "      <td>0.000008</td>\n",
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              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
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              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>normal</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
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              "      <td>0.000004</td>\n",
              "      <td>0.000004</td>\n",
              "      <td>0.000012</td>\n",
              "      <td>52</td>\n",
              "      <td>52</td>\n",
              "      <td>0</td>\n",
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              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>normal</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>?</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.425266e+09</td>\n",
              "      <td>0.000003</td>\n",
              "      <td>0.000003</td>\n",
              "      <td>0.000015</td>\n",
              "      <td>68</td>\n",
              "      <td>68</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>normal</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 155 columns</p>\n",
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              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
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              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
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              "\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
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              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "\n",
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              "  </div>\n"
            ],
            "text/plain": [
              "   frame.interface_id frame.dlt  frame.offset_shift  frame.time_epoch  \\\n",
              "0                   0         ?                 0.0      1.425266e+09   \n",
              "1                   0         ?                 0.0      1.425266e+09   \n",
              "2                   0         ?                 0.0      1.425266e+09   \n",
              "3                   0         ?                 0.0      1.425266e+09   \n",
              "4                   0         ?                 0.0      1.425266e+09   \n",
              "\n",
              "   frame.time_delta  frame.time_delta_displayed  frame.time_relative  \\\n",
              "0          0.000000                    0.000000             0.000000   \n",
              "1          0.000005                    0.000005             0.000005   \n",
              "2          0.000003                    0.000003             0.000008   \n",
              "3          0.000004                    0.000004             0.000012   \n",
              "4          0.000003                    0.000003             0.000015   \n",
              "\n",
              "   frame.len  frame.cap_len  frame.marked  ...  wlan.qos.priority  \\\n",
              "0         68             68             0  ...                  0   \n",
              "1         52             52             0  ...                  ?   \n",
              "2         66             66             0  ...                  ?   \n",
              "3         52             52             0  ...                  ?   \n",
              "4         68             68             0  ...                  0   \n",
              "\n",
              "   wlan.qos.eosp  wlan.qos.ack  wlan.qos.amsdupresent  \\\n",
              "0              ?        0x0000                      ?   \n",
              "1              ?             ?                      ?   \n",
              "2              ?             ?                      ?   \n",
              "3              ?             ?                      ?   \n",
              "4              ?        0x0000                      ?   \n",
              "\n",
              "   wlan.qos.buf_state_indicated  wlan.qos.bit4  wlan.qos.txop_dur_req  \\\n",
              "0                             ?              0                      0   \n",
              "1                             ?              ?                      ?   \n",
              "2                             ?              ?                      ?   \n",
              "3                             ?              ?                      ?   \n",
              "4                             ?              0                      0   \n",
              "\n",
              "   wlan.qos.buf_state_indicated.1  data.len   class  \n",
              "0                               ?         ?  normal  \n",
              "1                               ?         ?  normal  \n",
              "2                               ?         ?  normal  \n",
              "3                               ?         ?  normal  \n",
              "4                               ?         ?  normal  \n",
              "\n",
              "[5 rows x 155 columns]"
            ]
          },
          "execution_count": 27,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "import pandas as pd\n",
        "\n",
        "df = pd.read_csv('10.csv')\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 290
        },
        "id": "ysXRXKzGWOvE",
        "outputId": "17336a21-0ece-4ae3-caea-a839b3fe521a"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "<ipython-input-31-53efdae0f5f0>:3: DtypeWarning: Columns (60) have mixed types. Specify dtype option on import or set low_memory=False.\n",
            "  df = pd.read_csv('/content/drive/MyDrive/Dataset AWID/Zips/AWID2/AWID2_Dataset/9.csv')\n"
          ]
        },
        {
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              "      <td>10</td>\n",
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              "      border-bottom-color: var(--fill-color);\n",
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              "\n",
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              "\n",
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            ],
            "text/plain": [
              "   frame.interface_id frame.dlt  frame.offset_shift  frame.time_epoch  \\\n",
              "0                   0         ?                 0.0      1.425262e+09   \n",
              "1                   0         ?                 0.0      1.425262e+09   \n",
              "2                   0         ?                 0.0      1.425262e+09   \n",
              "3                   0         ?                 0.0      1.425262e+09   \n",
              "4                   0         ?                 0.0      1.425262e+09   \n",
              "\n",
              "   frame.time_delta  frame.time_delta_displayed  frame.time_relative  \\\n",
              "0          0.000000                    0.000000             0.000000   \n",
              "1          0.000007                    0.000007             0.000007   \n",
              "2          0.000007                    0.000007             0.000014   \n",
              "3          0.000768                    0.000768             0.000782   \n",
              "4          0.001442                    0.001442             0.002224   \n",
              "\n",
              "   frame.len  frame.cap_len  frame.marked  ...  wlan.qos.priority  \\\n",
              "0         52             52             0  ...                  ?   \n",
              "1        110            110             0  ...                  ?   \n",
              "2         52             52             0  ...                  ?   \n",
              "3        110            110             0  ...                  ?   \n",
              "4         84             84             0  ...                  ?   \n",
              "\n",
              "   wlan.qos.eosp  wlan.qos.ack  wlan.qos.amsdupresent  \\\n",
              "0              ?             ?                      ?   \n",
              "1              ?             ?                      ?   \n",
              "2              ?             ?                      ?   \n",
              "3              ?             ?                      ?   \n",
              "4              ?             ?                      ?   \n",
              "\n",
              "   wlan.qos.buf_state_indicated  wlan.qos.bit4  wlan.qos.txop_dur_req  \\\n",
              "0                             ?              ?                      ?   \n",
              "1                             ?              ?                      ?   \n",
              "2                             ?              ?                      ?   \n",
              "3                             ?              ?                      ?   \n",
              "4                             ?              ?                      ?   \n",
              "\n",
              "   wlan.qos.buf_state_indicated.1  data.len   class  \n",
              "0                               ?         ?  normal  \n",
              "1                               ?        36  normal  \n",
              "2                               ?         ?  normal  \n",
              "3                               ?        36  normal  \n",
              "4                               ?        10   hirte  \n",
              "\n",
              "[5 rows x 155 columns]"
            ]
          },
          "execution_count": 31,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "import pandas as pd\n",
        "\n",
        "df = pd.read_csv('/content/drive/MyDrive/Dataset AWID/Zips/AWID2/AWID2_Dataset/9.csv')\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 290
        },
        "id": "PpjcmljzWPSK",
        "outputId": "57f0ddd9-ae07-4e29-d238-81ed2ce59831"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "<ipython-input-32-322e88ddd0c4>:3: DtypeWarning: Columns (60,74,88) have mixed types. Specify dtype option on import or set low_memory=False.\n",
            "  df = pd.read_csv('/content/drive/MyDrive/Dataset AWID/Zips/AWID2/AWID2_Dataset/10.csv')\n"
          ]
        },
        {
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              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-d6db1827-610a-4790-9b66-8cbe40eddd33 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-d6db1827-610a-4790-9b66-8cbe40eddd33');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-333a18d4-bb34-4af4-b81b-3e4ebd7285bc\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-333a18d4-bb34-4af4-b81b-3e4ebd7285bc')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-333a18d4-bb34-4af4-b81b-3e4ebd7285bc button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "   frame.interface_id frame.dlt  frame.offset_shift  frame.time_epoch  \\\n",
              "0                   0         ?                 0.0      1.425266e+09   \n",
              "1                   0         ?                 0.0      1.425266e+09   \n",
              "2                   0         ?                 0.0      1.425266e+09   \n",
              "3                   0         ?                 0.0      1.425266e+09   \n",
              "4                   0         ?                 0.0      1.425266e+09   \n",
              "\n",
              "   frame.time_delta  frame.time_delta_displayed  frame.time_relative  \\\n",
              "0          0.000000                    0.000000             0.000000   \n",
              "1          0.000005                    0.000005             0.000005   \n",
              "2          0.000003                    0.000003             0.000008   \n",
              "3          0.000004                    0.000004             0.000012   \n",
              "4          0.000003                    0.000003             0.000015   \n",
              "\n",
              "   frame.len  frame.cap_len  frame.marked  ...  wlan.qos.priority  \\\n",
              "0         68             68             0  ...                  0   \n",
              "1         52             52             0  ...                  ?   \n",
              "2         66             66             0  ...                  ?   \n",
              "3         52             52             0  ...                  ?   \n",
              "4         68             68             0  ...                  0   \n",
              "\n",
              "   wlan.qos.eosp  wlan.qos.ack  wlan.qos.amsdupresent  \\\n",
              "0              ?        0x0000                      ?   \n",
              "1              ?             ?                      ?   \n",
              "2              ?             ?                      ?   \n",
              "3              ?             ?                      ?   \n",
              "4              ?        0x0000                      ?   \n",
              "\n",
              "   wlan.qos.buf_state_indicated  wlan.qos.bit4  wlan.qos.txop_dur_req  \\\n",
              "0                             ?              0                      0   \n",
              "1                             ?              ?                      ?   \n",
              "2                             ?              ?                      ?   \n",
              "3                             ?              ?                      ?   \n",
              "4                             ?              0                      0   \n",
              "\n",
              "   wlan.qos.buf_state_indicated.1  data.len   class  \n",
              "0                               ?         ?  normal  \n",
              "1                               ?         ?  normal  \n",
              "2                               ?         ?  normal  \n",
              "3                               ?         ?  normal  \n",
              "4                               ?         ?  normal  \n",
              "\n",
              "[5 rows x 155 columns]"
            ]
          },
          "execution_count": 32,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "import pandas as pd\n",
        "\n",
        "df = pd.read_csv('/content/drive/MyDrive/Dataset AWID/Zips/AWID2/AWID2_Dataset/10.csv')\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zS3z5QIrWtLQ"
      },
      "source": [
        "## 1.3 Evil Twin Dataset ( Making )"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_5n2Q781W6l_",
        "outputId": "1e05f500-3843-4e96-e0e5-19580f905bec"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "/content/drive/MyDrive/Dataset AWID/Zips/AWID2/AWID2_Dataset\n"
          ]
        }
      ],
      "source": [
        "cd /content/drive/MyDrive/Dataset AWID/Zips/AWID2/AWID2_Dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vscGYH9pDkNU",
        "outputId": "3418206e-9a42-4091-e0d1-0b310566f7f3"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "10.csv  72.csv  96.csv  9.csv  \u001b[0m\u001b[01;34mDATASET\u001b[0m/\n"
          ]
        }
      ],
      "source": [
        "ls"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XNEzwPQ2XFJs",
        "outputId": "4cc242a6-7b70-4ad7-8782-77b5743e3035"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "<ipython-input-35-711a8d6d31ae>:13: DtypeWarning: Columns (60) have mixed types. Specify dtype option on import or set low_memory=False.\n",
            "  data = pd.read_csv(file)\n",
            "<ipython-input-35-711a8d6d31ae>:16: DtypeWarning: Columns (60,74,88) have mixed types. Specify dtype option on import or set low_memory=False.\n",
            "  data = pd.read_csv(file, skiprows=1, header=None)\n",
            "<ipython-input-35-711a8d6d31ae>:16: DtypeWarning: Columns (60,61,74) have mixed types. Specify dtype option on import or set low_memory=False.\n",
            "  data = pd.read_csv(file, skiprows=1, header=None)\n",
            "<ipython-input-35-711a8d6d31ae>:16: DtypeWarning: Columns (60,74) have mixed types. Specify dtype option on import or set low_memory=False.\n",
            "  data = pd.read_csv(file, skiprows=1, header=None)\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Evil_Twin-Dataset.csv has been created successfully!\n"
          ]
        }
      ],
      "source": [
        "import pandas as pd\n",
        "\n",
        "# List of CSV files\n",
        "csv_files = ['9.csv', '10.csv', '72.csv', '96.csv']\n",
        "\n",
        "# Initialize an empty DataFrame\n",
        "combined_data = pd.DataFrame()\n",
        "\n",
        "# Iterate through each file\n",
        "for i, file in enumerate(csv_files):\n",
        "    if i == 0:\n",
        "        # Read the first file with the header\n",
        "        data = pd.read_csv(file)\n",
        "    else:\n",
        "        # Read subsequent files without the header\n",
        "        data = pd.read_csv(file, skiprows=1, header=None)\n",
        "        # Assign the columns from the first file to the new data\n",
        "        data.columns = combined_data.columns\n",
        "\n",
        "    # Filter rows with labels 'normal' and 'evil_twin'\n",
        "    filtered_data = data[data['class'].isin(['normal', 'evil_twin'])]\n",
        "\n",
        "    # Append the filtered data to the combined DataFrame\n",
        "    combined_data = pd.concat([combined_data, filtered_data], ignore_index=True)\n",
        "\n",
        "# Save the combined dataset to a new file\n",
        "combined_data.to_csv('Evil_Twin-Dataset.csv', index=False)\n",
        "\n",
        "print(\"Evil_Twin-Dataset.csv has been created successfully!\")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "oHk2Z1JyZdZ1",
        "outputId": "716bd88e-8004-4344-a293-de981085642f"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "evil_twin: 50643\n",
            "normal: 12998132\n"
          ]
        }
      ],
      "source": [
        "import csv\n",
        "\n",
        "counts = {}\n",
        "with open('Evil_Twin-Dataset.csv', 'r') as file:\n",
        "    reader = csv.reader(file)\n",
        "    header = next(reader)  # Skip the header row\n",
        "    for row in reader:\n",
        "        last_col = row[-1]\n",
        "        if last_col not in counts:\n",
        "            counts[last_col] = 0\n",
        "        counts[last_col] += 1\n",
        "\n",
        "unique_values = sorted(counts.keys())\n",
        "for value in unique_values:\n",
        "    print(f'{value}: {counts[value]}')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "pN0BENB5ZgFB",
        "outputId": "37334c36-840b-4271-8af7-4ccbe7893366"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "<ipython-input-37-9ab493641562>:3: DtypeWarning: Columns (1,31,32,60,61,74,88) have mixed types. Specify dtype option on import or set low_memory=False.\n",
            "  df = pd.read_csv('Evil_Twin-Dataset.csv')\n"
          ]
        },
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df"
            },
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              "  <div id=\"df-4277b4cd-602b-46f4-ba84-11bf4fe090ad\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
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              "\n",
              "    .dataframe thead th {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>frame.interface_id</th>\n",
              "      <th>frame.dlt</th>\n",
              "      <th>frame.offset_shift</th>\n",
              "      <th>frame.time_epoch</th>\n",
              "      <th>frame.time_delta</th>\n",
              "      <th>frame.time_delta_displayed</th>\n",
              "      <th>frame.time_relative</th>\n",
              "      <th>frame.len</th>\n",
              "      <th>frame.cap_len</th>\n",
              "      <th>frame.marked</th>\n",
              "      <th>...</th>\n",
              "      <th>wlan.qos.priority</th>\n",
              "      <th>wlan.qos.eosp</th>\n",
              "      <th>wlan.qos.ack</th>\n",
              "      <th>wlan.qos.amsdupresent</th>\n",
              "      <th>wlan.qos.buf_state_indicated</th>\n",
              "      <th>wlan.qos.bit4</th>\n",
              "      <th>wlan.qos.txop_dur_req</th>\n",
              "      <th>wlan.qos.buf_state_indicated.1</th>\n",
              "      <th>data.len</th>\n",
              "      <th>class</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
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              "      <td>0.000000</td>\n",
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              "      <td>?</td>\n",
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              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>normal</td>\n",
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              "      <td>?</td>\n",
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              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>36</td>\n",
              "      <td>normal</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>?</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.425262e+09</td>\n",
              "      <td>0.000007</td>\n",
              "      <td>0.000007</td>\n",
              "      <td>0.000014</td>\n",
              "      <td>52</td>\n",
              "      <td>52</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>normal</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>?</td>\n",
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              "      <td>1.425262e+09</td>\n",
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              "      <td>0.000768</td>\n",
              "      <td>0.000782</td>\n",
              "      <td>110</td>\n",
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              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>36</td>\n",
              "      <td>normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
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              "      <td>?</td>\n",
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              "      <td>0.000023</td>\n",
              "      <td>0.000023</td>\n",
              "      <td>0.002247</td>\n",
              "      <td>52</td>\n",
              "      <td>52</td>\n",
              "      <td>0</td>\n",
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              "      <td>?</td>\n",
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              "      <td>?</td>\n",
              "      <td>normal</td>\n",
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              "</table>\n",
              "<p>5 rows × 155 columns</p>\n",
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              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
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              "      fill: #D2E3FC;\n",
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              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-4277b4cd-602b-46f4-ba84-11bf4fe090ad');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "        docLink.innerHTML = docLinkHtml;\n",
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              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
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              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
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              "    box-shadow: none;\n",
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              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
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              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "\n",
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              "  </div>\n"
            ],
            "text/plain": [
              "   frame.interface_id frame.dlt  frame.offset_shift  frame.time_epoch  \\\n",
              "0                   0         ?                 0.0      1.425262e+09   \n",
              "1                   0         ?                 0.0      1.425262e+09   \n",
              "2                   0         ?                 0.0      1.425262e+09   \n",
              "3                   0         ?                 0.0      1.425262e+09   \n",
              "4                   0         ?                 0.0      1.425262e+09   \n",
              "\n",
              "   frame.time_delta  frame.time_delta_displayed  frame.time_relative  \\\n",
              "0          0.000000                    0.000000             0.000000   \n",
              "1          0.000007                    0.000007             0.000007   \n",
              "2          0.000007                    0.000007             0.000014   \n",
              "3          0.000768                    0.000768             0.000782   \n",
              "4          0.000023                    0.000023             0.002247   \n",
              "\n",
              "   frame.len  frame.cap_len  frame.marked  ...  wlan.qos.priority  \\\n",
              "0         52             52             0  ...                  ?   \n",
              "1        110            110             0  ...                  ?   \n",
              "2         52             52             0  ...                  ?   \n",
              "3        110            110             0  ...                  ?   \n",
              "4         52             52             0  ...                  ?   \n",
              "\n",
              "   wlan.qos.eosp  wlan.qos.ack  wlan.qos.amsdupresent  \\\n",
              "0              ?             ?                      ?   \n",
              "1              ?             ?                      ?   \n",
              "2              ?             ?                      ?   \n",
              "3              ?             ?                      ?   \n",
              "4              ?             ?                      ?   \n",
              "\n",
              "   wlan.qos.buf_state_indicated  wlan.qos.bit4  wlan.qos.txop_dur_req  \\\n",
              "0                             ?              ?                      ?   \n",
              "1                             ?              ?                      ?   \n",
              "2                             ?              ?                      ?   \n",
              "3                             ?              ?                      ?   \n",
              "4                             ?              ?                      ?   \n",
              "\n",
              "   wlan.qos.buf_state_indicated.1  data.len   class  \n",
              "0                               ?         ?  normal  \n",
              "1                               ?        36  normal  \n",
              "2                               ?         ?  normal  \n",
              "3                               ?        36  normal  \n",
              "4                               ?         ?  normal  \n",
              "\n",
              "[5 rows x 155 columns]"
            ]
          },
          "execution_count": 37,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "import pandas as pd\n",
        "\n",
        "df = pd.read_csv('Evil_Twin-Dataset.csv')\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6QESclGRZ7rn"
      },
      "source": [
        "# 2. Preprocessing & Model Tranning ( Random Forest, KNN, Naive Bayes)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0xslyYtva2wT"
      },
      "source": [
        "## 2.1 Initial Preprocessing"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0L-7EjXebG5W",
        "outputId": "cf1be60a-3559-44e0-98d6-411b0f514ad7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/drive/MyDrive/Dataset AWID/Zips/AWID2/AWID2_Dataset\n"
          ]
        }
      ],
      "source": [
        "cd /content/drive/MyDrive/Dataset AWID/Zips/AWID2/AWID2_Dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zr-vVZgpDGSZ",
        "outputId": "4d5c93af-be66-4223-e59f-a67330ca117b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "10.csv  72.csv  96.csv  9.csv  \u001b[0m\u001b[01;34mDATASET\u001b[0m/  Evil_Twin-Dataset.csv\n"
          ]
        }
      ],
      "source": [
        "ls"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 290
        },
        "id": "RNykk5fCcxw1",
        "outputId": "b9864a69-c4a9-4bcf-bde6-60c4c1ac1bd7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-42-9ab493641562>:3: DtypeWarning: Columns (1,31,32,60,61,74,88) have mixed types. Specify dtype option on import or set low_memory=False.\n",
            "  df = pd.read_csv('Evil_Twin-Dataset.csv')\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
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              "   frame.interface_id frame.dlt  frame.offset_shift  frame.time_epoch  \\\n",
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              "1                   0         ?                 0.0      1.425262e+09   \n",
              "2                   0         ?                 0.0      1.425262e+09   \n",
              "3                   0         ?                 0.0      1.425262e+09   \n",
              "4                   0         ?                 0.0      1.425262e+09   \n",
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              "   frame.time_delta  frame.time_delta_displayed  frame.time_relative  \\\n",
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              "3          0.000768                    0.000768             0.000782   \n",
              "4          0.000023                    0.000023             0.002247   \n",
              "\n",
              "   frame.len  frame.cap_len  frame.marked  ...  wlan.qos.priority  \\\n",
              "0         52             52             0  ...                  ?   \n",
              "1        110            110             0  ...                  ?   \n",
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              "   wlan.qos.eosp  wlan.qos.ack  wlan.qos.amsdupresent  \\\n",
              "0              ?             ?                      ?   \n",
              "1              ?             ?                      ?   \n",
              "2              ?             ?                      ?   \n",
              "3              ?             ?                      ?   \n",
              "4              ?             ?                      ?   \n",
              "\n",
              "   wlan.qos.buf_state_indicated  wlan.qos.bit4  wlan.qos.txop_dur_req  \\\n",
              "0                             ?              ?                      ?   \n",
              "1                             ?              ?                      ?   \n",
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              "4                             ?              ?                      ?   \n",
              "\n",
              "   wlan.qos.buf_state_indicated.1  data.len   class  \n",
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              "2                               ?         ?  normal  \n",
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              "4                               ?         ?  normal  \n",
              "\n",
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              "      <td>1.425262e+09</td>\n",
              "      <td>0.000768</td>\n",
              "      <td>0.000768</td>\n",
              "      <td>0.000782</td>\n",
              "      <td>110</td>\n",
              "      <td>110</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>36</td>\n",
              "      <td>normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>?</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.425262e+09</td>\n",
              "      <td>0.000023</td>\n",
              "      <td>0.000023</td>\n",
              "      <td>0.002247</td>\n",
              "      <td>52</td>\n",
              "      <td>52</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>normal</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 155 columns</p>\n",
              "</div>\n",
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              "\n",
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              "    .colab-df-convert:hover {\n",
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              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-ed0e2242-0181-45d8-8107-f5bae32d9ca6');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "  </button>\n",
              "\n",
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              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
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              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
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              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
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              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-efba5413-a3c9-4dc4-a6c3-f12df1feb7e7 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df"
            }
          },
          "metadata": {},
          "execution_count": 42
        }
      ],
      "source": [
        "import pandas as pd\n",
        "\n",
        "df = pd.read_csv('Evil_Twin-Dataset.csv')\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 44,
      "metadata": {
        "id": "zgxJiNUSc4Aq",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4e8710d7-7971-48e8-d0ed-b0a49615a8be"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-44-3008753773f9>:4: DtypeWarning: Columns (1,31,32,60,61,74,88) have mixed types. Specify dtype option on import or set low_memory=False.\n",
            "  df = pd.read_csv('Evil_Twin-Dataset.csv')\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Columns with equal values in all rows: ['frame.interface_id', 'frame.offset_shift', 'frame.marked', 'frame.ignored', 'radiotap.version', 'radiotap.pad', 'radiotap.present.tsft', 'radiotap.present.flags', 'radiotap.present.channel', 'radiotap.present.fhss', 'radiotap.present.dbm_antnoise', 'radiotap.present.lock_quality', 'radiotap.present.tx_attenuation', 'radiotap.present.db_tx_attenuation', 'radiotap.present.dbm_tx_power', 'radiotap.present.db_antsignal', 'radiotap.present.db_antnoise', 'radiotap.present.rxflags', 'radiotap.present.xchannel', 'radiotap.present.vendor_ns', 'radiotap.flags.cfp', 'radiotap.flags.wep', 'radiotap.flags.frag', 'radiotap.flags.fcs', 'radiotap.flags.datapad', 'radiotap.flags.badfcs', 'radiotap.flags.shortgi', 'radiotap.channel.freq', 'radiotap.channel.type.turbo', 'radiotap.channel.type.cck', 'radiotap.channel.type.2ghz', 'radiotap.channel.type.5ghz', 'radiotap.channel.type.passive', 'radiotap.channel.type.gfsk', 'radiotap.channel.type.gsm', 'radiotap.channel.type.sturbo', 'radiotap.channel.type.half', 'radiotap.channel.type.quarter', 'radiotap.rxflags.badplcp', 'wlan_mgt.fixed.htact', 'wlan_mgt.fixed.chanwidth', 'wlan_mgt.tcprep.trsmt_pow', 'wlan_mgt.tcprep.link_mrg', 'wlan.qos.buf_state_indicated']\n"
          ]
        }
      ],
      "source": [
        "import pandas as pd\n",
        "\n",
        "# Read the CSV file into a DataFrame\n",
        "df = pd.read_csv('Evil_Twin-Dataset.csv')\n",
        "\n",
        "# Initialize an empty list to store column names with equal values\n",
        "equal_columns = []\n",
        "\n",
        "# Iterate over each column in the dataset\n",
        "for column in df.columns:\n",
        "    # Check if all values in the column are equal\n",
        "    if all(df[column] == df[column].iloc[0]):\n",
        "        # If all values are equal, add the column name to the list\n",
        "        equal_columns.append(column)\n",
        "\n",
        "# Print the list of columns with equal values\n",
        "print(\"Columns with equal values in all rows:\", equal_columns)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 47,
      "metadata": {
        "id": "qMBzN48QdGKM",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c87a22b4-a1a4-4e1e-f17e-a2d9b3b8dbc9"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-47-5113be93d98a>:3: DtypeWarning: Columns (1,31,32,60,61,74,88) have mixed types. Specify dtype option on import or set low_memory=False.\n",
            "  df = pd.read_csv('Evil_Twin-Dataset.csv')\n"
          ]
        }
      ],
      "source": [
        "import pandas as pd\n",
        "\n",
        "df = pd.read_csv('Evil_Twin-Dataset.csv')\n",
        "# Columns to remove\n",
        "columns_to_remove = [\n",
        "    'wlan.fcs_good', 'radiotap.channel.freq', 'radiotap.channel.type.2ghz', 'radiotap.channel.type.half', 'wlan.fc.order', 'radiotap.channel.type.passive', 'radiotap.channel.type.gsm', 'wlan_mgt.tcprep.link_mrg', 'radiotap.present.db_antsignal', 'wlan.qos.buf_state_indicated', 'radiotap.present.tsft', 'radiotap.present.db_antnoise', 'radiotap.channel.type.sturbo', 'radiotap.present.rxflags', 'radiotap.flags.fcs', 'radiotap.channel.type.quarter', 'radiotap.channel.type.5ghz', 'radiotap.present.db_tx_attenuation', 'radiotap.flags.frag', 'radiotap.present.dbm_tx_power', 'radiotap.present.xchannel', 'radiotap.present.dbm_antnoise', 'radiotap.present.tx_attenuation', 'radiotap.present.vht', 'radiotap.channel.type.gfsk', 'frame.marked', 'radiotap.channel.type.turbo', 'radiotap.flags.datapad', 'wlan_mgt.fixed.chanwidth', 'radiotap.present.lock_quality', 'frame.interface_id', 'radiotap.present.reserved', 'radiotap.flags.wep', 'radiotap.flags.badfcs', 'radiotap.present.fhss', 'radiotap.version', 'wlan.fc.version', 'wlan_mgt.tcprep.trsmt_pow', 'radiotap.pad', 'frame.offset_shift', 'wlan_mgt.fixed.htact', 'radiotap.present.channel', 'radiotap.rxflags.badplcp', 'radiotap.present.vendor_ns', 'radiotap.present.flags', 'wlan.fc.frag', 'frame.dlt', 'frame.ignored', 'radiotap.flags.cfp', 'radiotap.flags.shortgi', 'radiotap.channel.type.cck'\n",
        "]\n",
        "\n",
        "# Drop the specified columns\n",
        "df.drop(columns=columns_to_remove, inplace=True)\n",
        "\n",
        "# Save the modified DataFrame to a new CSV file\n",
        "# Replace 'remove96.csv' with the desired name and path for the new CSV file\n",
        "df.to_csv('Evil_Twin-Dataset_Updated.csv', index=False)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ls"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7FS-79BKMdpJ",
        "outputId": "0e6c0a20-079e-4a77-ac0a-ad715be337d3"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "10.csv  72.csv  96.csv  9.csv  \u001b[0m\u001b[01;34mDATASET\u001b[0m/  Evil_Twin-Dataset.csv  Evil_Twin-Dataset_Updated.csv\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 49,
      "metadata": {
        "id": "RKwGpzkrdoDg",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 290
        },
        "outputId": "847a4672-0bb8-434b-d72f-4216d9534818"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-49-900cffb0630f>:3: DtypeWarning: Columns (11,19,20,29) have mixed types. Specify dtype option on import or set low_memory=False.\n",
            "  df = pd.read_csv('Evil_Twin-Dataset_Updated.csv')\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   frame.time_epoch  frame.time_delta  frame.time_delta_displayed  \\\n",
              "0      1.425262e+09          0.000000                    0.000000   \n",
              "1      1.425262e+09          0.000007                    0.000007   \n",
              "2      1.425262e+09          0.000007                    0.000007   \n",
              "3      1.425262e+09          0.000768                    0.000768   \n",
              "4      1.425262e+09          0.000023                    0.000023   \n",
              "\n",
              "   frame.time_relative  frame.len  frame.cap_len  radiotap.length  \\\n",
              "0             0.000000         52             52               38   \n",
              "1             0.000007        110            110               38   \n",
              "2             0.000014         52             52               38   \n",
              "3             0.000782        110            110               38   \n",
              "4             0.002247         52             52               38   \n",
              "\n",
              "   radiotap.present.rate  radiotap.present.dbm_antsignal  \\\n",
              "0                      1                               1   \n",
              "1                      1                               1   \n",
              "2                      1                               1   \n",
              "3                      1                               1   \n",
              "4                      1                               1   \n",
              "\n",
              "   radiotap.present.antenna  ...  wlan.qos.tid wlan.qos.priority  \\\n",
              "0                         0  ...             ?                 ?   \n",
              "1                         0  ...             ?                 ?   \n",
              "2                         0  ...             ?                 ?   \n",
              "3                         0  ...             ?                 ?   \n",
              "4                         0  ...             ?                 ?   \n",
              "\n",
              "   wlan.qos.eosp  wlan.qos.ack  wlan.qos.amsdupresent  wlan.qos.bit4  \\\n",
              "0              ?             ?                      ?              ?   \n",
              "1              ?             ?                      ?              ?   \n",
              "2              ?             ?                      ?              ?   \n",
              "3              ?             ?                      ?              ?   \n",
              "4              ?             ?                      ?              ?   \n",
              "\n",
              "   wlan.qos.txop_dur_req  wlan.qos.buf_state_indicated.1  data.len   class  \n",
              "0                      ?                               ?         ?  normal  \n",
              "1                      ?                               ?        36  normal  \n",
              "2                      ?                               ?         ?  normal  \n",
              "3                      ?                               ?        36  normal  \n",
              "4                      ?                               ?         ?  normal  \n",
              "\n",
              "[5 rows x 104 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-0a2dfb6d-5218-4077-89b6-35cd838343bb\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>frame.time_epoch</th>\n",
              "      <th>frame.time_delta</th>\n",
              "      <th>frame.time_delta_displayed</th>\n",
              "      <th>frame.time_relative</th>\n",
              "      <th>frame.len</th>\n",
              "      <th>frame.cap_len</th>\n",
              "      <th>radiotap.length</th>\n",
              "      <th>radiotap.present.rate</th>\n",
              "      <th>radiotap.present.dbm_antsignal</th>\n",
              "      <th>radiotap.present.antenna</th>\n",
              "      <th>...</th>\n",
              "      <th>wlan.qos.tid</th>\n",
              "      <th>wlan.qos.priority</th>\n",
              "      <th>wlan.qos.eosp</th>\n",
              "      <th>wlan.qos.ack</th>\n",
              "      <th>wlan.qos.amsdupresent</th>\n",
              "      <th>wlan.qos.bit4</th>\n",
              "      <th>wlan.qos.txop_dur_req</th>\n",
              "      <th>wlan.qos.buf_state_indicated.1</th>\n",
              "      <th>data.len</th>\n",
              "      <th>class</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1.425262e+09</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>52</td>\n",
              "      <td>52</td>\n",
              "      <td>38</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1.425262e+09</td>\n",
              "      <td>0.000007</td>\n",
              "      <td>0.000007</td>\n",
              "      <td>0.000007</td>\n",
              "      <td>110</td>\n",
              "      <td>110</td>\n",
              "      <td>38</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>36</td>\n",
              "      <td>normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1.425262e+09</td>\n",
              "      <td>0.000007</td>\n",
              "      <td>0.000007</td>\n",
              "      <td>0.000014</td>\n",
              "      <td>52</td>\n",
              "      <td>52</td>\n",
              "      <td>38</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1.425262e+09</td>\n",
              "      <td>0.000768</td>\n",
              "      <td>0.000768</td>\n",
              "      <td>0.000782</td>\n",
              "      <td>110</td>\n",
              "      <td>110</td>\n",
              "      <td>38</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>36</td>\n",
              "      <td>normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1.425262e+09</td>\n",
              "      <td>0.000023</td>\n",
              "      <td>0.000023</td>\n",
              "      <td>0.002247</td>\n",
              "      <td>52</td>\n",
              "      <td>52</td>\n",
              "      <td>38</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>normal</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 104 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0a2dfb6d-5218-4077-89b6-35cd838343bb')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-0a2dfb6d-5218-4077-89b6-35cd838343bb button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-0a2dfb6d-5218-4077-89b6-35cd838343bb');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-dfe7a43c-6752-47b3-b560-d6b5583dddc0\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-dfe7a43c-6752-47b3-b560-d6b5583dddc0')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-dfe7a43c-6752-47b3-b560-d6b5583dddc0 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df"
            }
          },
          "metadata": {},
          "execution_count": 49
        }
      ],
      "source": [
        "import pandas as pd\n",
        "\n",
        "df = pd.read_csv('Evil_Twin-Dataset_Updated.csv')\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 50,
      "metadata": {
        "id": "71rYJJczfV9j",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ad692d6a-e3e3-4a96-e8f7-05c9a9f581e0"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 13048775 entries, 0 to 13048774\n",
            "Columns: 104 entries, frame.time_epoch to class\n",
            "dtypes: float64(5), int64(19), object(80)\n",
            "memory usage: 10.1+ GB\n"
          ]
        }
      ],
      "source": [
        "df.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 51,
      "metadata": {
        "id": "M8WWXB4KfYl-",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b3ce7052-d0e6-4bd0-e672-ddb395d26a79"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Class counts in the DataFrame: {'normal': 12998132, 'evil_twin': 50643}\n",
            "normal: 12998132\n",
            "evil_twin: 50643\n"
          ]
        }
      ],
      "source": [
        "def unique_class_counts(df, target_column):\n",
        "    class_counts = df[target_column].value_counts().to_dict()\n",
        "    return class_counts\n",
        "\n",
        "# Get unique class counts\n",
        "class_counts = unique_class_counts(df, 'class')\n",
        "print(f\"Class counts in the DataFrame: {class_counts}\")\n",
        "for class_name, count in class_counts.items():\n",
        "    print(f\"{class_name}: {count}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 52,
      "metadata": {
        "id": "MhCefWDvfbMF",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "66a191e3-0f35-4041-b73c-361148dad820"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "104\n",
            "['frame.time_epoch', 'frame.time_delta', 'frame.time_delta_displayed', 'frame.time_relative', 'frame.len', 'frame.cap_len', 'radiotap.length', 'radiotap.present.rate', 'radiotap.present.dbm_antsignal', 'radiotap.present.antenna', 'radiotap.present.mcs', 'radiotap.present.ampdu', 'radiotap.present.rtap_ns', 'radiotap.present.ext', 'radiotap.mactime', 'radiotap.flags.preamble', 'radiotap.datarate', 'radiotap.channel.type.ofdm', 'radiotap.channel.type.dynamic', 'radiotap.dbm_antsignal', 'radiotap.antenna', 'wlan.fc.type_subtype', 'wlan.fc.type', 'wlan.fc.subtype', 'wlan.fc.ds', 'wlan.fc.retry', 'wlan.fc.pwrmgt', 'wlan.fc.moredata', 'wlan.fc.protected', 'wlan.duration', 'wlan.ra', 'wlan.da', 'wlan.ta', 'wlan.sa', 'wlan.bssid', 'wlan.frag', 'wlan.seq', 'wlan.bar.type', 'wlan.ba.control.ackpolicy', 'wlan.ba.control.multitid', 'wlan.ba.control.cbitmap', 'wlan.bar.compressed.tidinfo', 'wlan.ba.bm', 'wlan_mgt.fixed.capabilities.ess', 'wlan_mgt.fixed.capabilities.ibss', 'wlan_mgt.fixed.capabilities.cfpoll.ap', 'wlan_mgt.fixed.capabilities.privacy', 'wlan_mgt.fixed.capabilities.preamble', 'wlan_mgt.fixed.capabilities.pbcc', 'wlan_mgt.fixed.capabilities.agility', 'wlan_mgt.fixed.capabilities.spec_man', 'wlan_mgt.fixed.capabilities.short_slot_time', 'wlan_mgt.fixed.capabilities.apsd', 'wlan_mgt.fixed.capabilities.radio_measurement', 'wlan_mgt.fixed.capabilities.dsss_ofdm', 'wlan_mgt.fixed.capabilities.del_blk_ack', 'wlan_mgt.fixed.capabilities.imm_blk_ack', 'wlan_mgt.fixed.listen_ival', 'wlan_mgt.fixed.current_ap', 'wlan_mgt.fixed.status_code', 'wlan_mgt.fixed.timestamp', 'wlan_mgt.fixed.beacon', 'wlan_mgt.fixed.aid', 'wlan_mgt.fixed.reason_code', 'wlan_mgt.fixed.auth.alg', 'wlan_mgt.fixed.auth_seq', 'wlan_mgt.fixed.category_code', 'wlan_mgt.fixed.fragment', 'wlan_mgt.fixed.sequence', 'wlan_mgt.tagged.all', 'wlan_mgt.ssid', 'wlan_mgt.ds.current_channel', 'wlan_mgt.tim.dtim_count', 'wlan_mgt.tim.dtim_period', 'wlan_mgt.tim.bmapctl.multicast', 'wlan_mgt.tim.bmapctl.offset', 'wlan_mgt.country_info.environment', 'wlan_mgt.rsn.version', 'wlan_mgt.rsn.gcs.type', 'wlan_mgt.rsn.pcs.count', 'wlan_mgt.rsn.akms.count', 'wlan_mgt.rsn.akms.type', 'wlan_mgt.rsn.capabilities.preauth', 'wlan_mgt.rsn.capabilities.no_pairwise', 'wlan_mgt.rsn.capabilities.ptksa_replay_counter', 'wlan_mgt.rsn.capabilities.gtksa_replay_counter', 'wlan_mgt.rsn.capabilities.mfpr', 'wlan_mgt.rsn.capabilities.mfpc', 'wlan_mgt.rsn.capabilities.peerkey', 'wlan.wep.iv', 'wlan.wep.key', 'wlan.wep.icv', 'wlan.tkip.extiv', 'wlan.ccmp.extiv', 'wlan.qos.tid', 'wlan.qos.priority', 'wlan.qos.eosp', 'wlan.qos.ack', 'wlan.qos.amsdupresent', 'wlan.qos.bit4', 'wlan.qos.txop_dur_req', 'wlan.qos.buf_state_indicated.1', 'data.len', 'class']\n"
          ]
        }
      ],
      "source": [
        "column_names_list = df.columns.tolist() # Column count and name of the columns\n",
        "print(len(column_names_list))\n",
        "print(column_names_list)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pDaliJPDbHWU"
      },
      "source": [
        "## 2.2 Random Forest Model and Feather Preprocessing"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hxStHZenb3bD"
      },
      "source": [
        "### 2.2.1 Preprocessing"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 53,
      "metadata": {
        "id": "Ce8REbZLflEH",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "41b33f99-d610-45e3-b85f-f08afe4f5878"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/drive/MyDrive/Dataset AWID/Zips/AWID2/AWID2_Dataset\n"
          ]
        }
      ],
      "source": [
        "cd /content/drive/MyDrive/Dataset AWID/Zips/AWID2/AWID2_Dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 54,
      "metadata": {
        "id": "ebT5RQW4biWG",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 290
        },
        "outputId": "980df4d5-032a-445e-f70f-fe57920bb4cd"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-54-900cffb0630f>:3: DtypeWarning: Columns (11,19,20,29) have mixed types. Specify dtype option on import or set low_memory=False.\n",
            "  df = pd.read_csv('Evil_Twin-Dataset_Updated.csv')\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   frame.time_epoch  frame.time_delta  frame.time_delta_displayed  \\\n",
              "0      1.425262e+09          0.000000                    0.000000   \n",
              "1      1.425262e+09          0.000007                    0.000007   \n",
              "2      1.425262e+09          0.000007                    0.000007   \n",
              "3      1.425262e+09          0.000768                    0.000768   \n",
              "4      1.425262e+09          0.000023                    0.000023   \n",
              "\n",
              "   frame.time_relative  frame.len  frame.cap_len  radiotap.length  \\\n",
              "0             0.000000         52             52               38   \n",
              "1             0.000007        110            110               38   \n",
              "2             0.000014         52             52               38   \n",
              "3             0.000782        110            110               38   \n",
              "4             0.002247         52             52               38   \n",
              "\n",
              "   radiotap.present.rate  radiotap.present.dbm_antsignal  \\\n",
              "0                      1                               1   \n",
              "1                      1                               1   \n",
              "2                      1                               1   \n",
              "3                      1                               1   \n",
              "4                      1                               1   \n",
              "\n",
              "   radiotap.present.antenna  ...  wlan.qos.tid wlan.qos.priority  \\\n",
              "0                         0  ...             ?                 ?   \n",
              "1                         0  ...             ?                 ?   \n",
              "2                         0  ...             ?                 ?   \n",
              "3                         0  ...             ?                 ?   \n",
              "4                         0  ...             ?                 ?   \n",
              "\n",
              "   wlan.qos.eosp  wlan.qos.ack  wlan.qos.amsdupresent  wlan.qos.bit4  \\\n",
              "0              ?             ?                      ?              ?   \n",
              "1              ?             ?                      ?              ?   \n",
              "2              ?             ?                      ?              ?   \n",
              "3              ?             ?                      ?              ?   \n",
              "4              ?             ?                      ?              ?   \n",
              "\n",
              "   wlan.qos.txop_dur_req  wlan.qos.buf_state_indicated.1  data.len   class  \n",
              "0                      ?                               ?         ?  normal  \n",
              "1                      ?                               ?        36  normal  \n",
              "2                      ?                               ?         ?  normal  \n",
              "3                      ?                               ?        36  normal  \n",
              "4                      ?                               ?         ?  normal  \n",
              "\n",
              "[5 rows x 104 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-f2fb3263-802b-4d8c-b98d-7c25c525f5e3\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "</style>\n",
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              "      <th></th>\n",
              "      <th>frame.time_epoch</th>\n",
              "      <th>frame.time_delta</th>\n",
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              "      <th>frame.time_relative</th>\n",
              "      <th>frame.len</th>\n",
              "      <th>frame.cap_len</th>\n",
              "      <th>radiotap.length</th>\n",
              "      <th>radiotap.present.rate</th>\n",
              "      <th>radiotap.present.dbm_antsignal</th>\n",
              "      <th>radiotap.present.antenna</th>\n",
              "      <th>...</th>\n",
              "      <th>wlan.qos.tid</th>\n",
              "      <th>wlan.qos.priority</th>\n",
              "      <th>wlan.qos.eosp</th>\n",
              "      <th>wlan.qos.ack</th>\n",
              "      <th>wlan.qos.amsdupresent</th>\n",
              "      <th>wlan.qos.bit4</th>\n",
              "      <th>wlan.qos.txop_dur_req</th>\n",
              "      <th>wlan.qos.buf_state_indicated.1</th>\n",
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              "      <th>0</th>\n",
              "      <td>1.425262e+09</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>52</td>\n",
              "      <td>52</td>\n",
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              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>normal</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1.425262e+09</td>\n",
              "      <td>0.000007</td>\n",
              "      <td>0.000007</td>\n",
              "      <td>0.000007</td>\n",
              "      <td>110</td>\n",
              "      <td>110</td>\n",
              "      <td>38</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
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              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>36</td>\n",
              "      <td>normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1.425262e+09</td>\n",
              "      <td>0.000007</td>\n",
              "      <td>0.000007</td>\n",
              "      <td>0.000014</td>\n",
              "      <td>52</td>\n",
              "      <td>52</td>\n",
              "      <td>38</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1.425262e+09</td>\n",
              "      <td>0.000768</td>\n",
              "      <td>0.000768</td>\n",
              "      <td>0.000782</td>\n",
              "      <td>110</td>\n",
              "      <td>110</td>\n",
              "      <td>38</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
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              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>36</td>\n",
              "      <td>normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1.425262e+09</td>\n",
              "      <td>0.000023</td>\n",
              "      <td>0.000023</td>\n",
              "      <td>0.002247</td>\n",
              "      <td>52</td>\n",
              "      <td>52</td>\n",
              "      <td>38</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>normal</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 104 columns</p>\n",
              "</div>\n",
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              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "\n",
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              "      display:flex;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "      border-radius: 50%;\n",
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              "      fill: #1967D2;\n",
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              "\n",
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              "\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-f2fb3263-802b-4d8c-b98d-7c25c525f5e3 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-f2fb3263-802b-4d8c-b98d-7c25c525f5e3');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "    </script>\n",
              "  </div>\n",
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              "            title=\"Suggest charts\"\n",
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              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
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              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
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              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
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              "\n",
              "  .colab-df-quickchart {\n",
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              "\n",
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              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "    background-color: var(--disabled-bg-color);\n",
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              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
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              "      border-right-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-0c9ef530-3f0b-4319-8e8a-4521b2bf3c71 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df"
            }
          },
          "metadata": {},
          "execution_count": 54
        }
      ],
      "source": [
        "import pandas as pd\n",
        "\n",
        "df = pd.read_csv('Evil_Twin-Dataset_Updated.csv')\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 55,
      "metadata": {
        "id": "uMlbPEVnfkJZ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "340beec3-dee0-4098-f84f-ee5015af51c8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Class counts in the DataFrame: {'normal': 12998132, 'evil_twin': 50643}\n",
            "normal: 12998132\n",
            "evil_twin: 50643\n"
          ]
        }
      ],
      "source": [
        "def unique_class_counts(df, target_column):\n",
        "    class_counts = df[target_column].value_counts().to_dict()\n",
        "    return class_counts\n",
        "\n",
        "# Get unique class counts\n",
        "class_counts = unique_class_counts(df, 'class')\n",
        "print(f\"Class counts in the DataFrame: {class_counts}\")\n",
        "for class_name, count in class_counts.items():\n",
        "    print(f\"{class_name}: {count}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 56,
      "metadata": {
        "id": "PqV4Sw19fsYi"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "#Replace ? to NaN\n",
        "pre_num_cols = len(df.columns)\n",
        "df.replace('?', np.nan, inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 57,
      "metadata": {
        "id": "qz-SK-32fvgW",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "82423324-bb48-477d-d7e5-8dc8386d9acc"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of columns dropped: 50\n"
          ]
        }
      ],
      "source": [
        "import numpy as np\n",
        "#Remove Columns (Featuers) which has more than 90% missing values\n",
        "pre_num_cols = len(df.columns)\n",
        "df.replace('?', np.nan, inplace=True)\n",
        "df.dropna(axis='columns', thresh=len(df.index)*0.10, inplace=True)\n",
        "\n",
        "\n",
        "post_num_cols = len(df.columns)\n",
        "print(f\"Number of columns dropped: {pre_num_cols - post_num_cols}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 58,
      "metadata": {
        "id": "AizYmGiBf7sj",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        },
        "outputId": "57ee7752-c875-4ddc-96ee-265db424d015"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   frame.time_epoch  frame.time_delta  frame.time_delta_displayed  \\\n",
              "0      1.425262e+09          0.000000                    0.000000   \n",
              "1      1.425262e+09          0.000007                    0.000007   \n",
              "2      1.425262e+09          0.000007                    0.000007   \n",
              "3      1.425262e+09          0.000768                    0.000768   \n",
              "4      1.425262e+09          0.000023                    0.000023   \n",
              "\n",
              "   frame.time_relative  frame.len  frame.cap_len  radiotap.length  \\\n",
              "0             0.000000         52             52               38   \n",
              "1             0.000007        110            110               38   \n",
              "2             0.000014         52             52               38   \n",
              "3             0.000782        110            110               38   \n",
              "4             0.002247         52             52               38   \n",
              "\n",
              "   radiotap.present.rate  radiotap.present.dbm_antsignal  \\\n",
              "0                      1                               1   \n",
              "1                      1                               1   \n",
              "2                      1                               1   \n",
              "3                      1                               1   \n",
              "4                      1                               1   \n",
              "\n",
              "   radiotap.present.antenna  ...  wlan.wep.key wlan.wep.icv  wlan.qos.tid  \\\n",
              "0                         0  ...           NaN          NaN           NaN   \n",
              "1                         0  ...             0   0xbf54e133           NaN   \n",
              "2                         0  ...           NaN          NaN           NaN   \n",
              "3                         0  ...             0   0xe1688921           NaN   \n",
              "4                         0  ...           NaN          NaN           NaN   \n",
              "\n",
              "   wlan.qos.priority  wlan.qos.ack  wlan.qos.amsdupresent  wlan.qos.bit4  \\\n",
              "0                NaN           NaN                    NaN            NaN   \n",
              "1                NaN           NaN                    NaN            NaN   \n",
              "2                NaN           NaN                    NaN            NaN   \n",
              "3                NaN           NaN                    NaN            NaN   \n",
              "4                NaN           NaN                    NaN            NaN   \n",
              "\n",
              "   wlan.qos.txop_dur_req  data.len   class  \n",
              "0                    NaN       NaN  normal  \n",
              "1                    NaN        36  normal  \n",
              "2                    NaN       NaN  normal  \n",
              "3                    NaN        36  normal  \n",
              "4                    NaN       NaN  normal  \n",
              "\n",
              "[5 rows x 54 columns]"
            ],
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              "      <th>radiotap.present.dbm_antsignal</th>\n",
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              "      <th>wlan.wep.icv</th>\n",
              "      <th>wlan.qos.tid</th>\n",
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              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
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              "      <td>NaN</td>\n",
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              "      <th>1</th>\n",
              "      <td>1.425262e+09</td>\n",
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              "      <td>0.000007</td>\n",
              "      <td>0.000007</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <th>2</th>\n",
              "      <td>1.425262e+09</td>\n",
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              "      <td>0.000014</td>\n",
              "      <td>52</td>\n",
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              "      <td>1</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <th>3</th>\n",
              "      <td>1.425262e+09</td>\n",
              "      <td>0.000768</td>\n",
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              "      <td>0.000782</td>\n",
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              "      <td>1</td>\n",
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              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
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              "      <td>36</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1.425262e+09</td>\n",
              "      <td>0.000023</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>normal</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 54 columns</p>\n",
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              "  </style>\n",
              "\n",
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              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "          + ' to learn more about interactive tables.';\n",
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              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "    background-color: var(--disabled-bg-color);\n",
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              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
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              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "      border-right-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "      border-bottom-color: var(--fill-color);\n",
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              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-3f2eec5c-d24e-4be8-8800-ce4638eabfa1 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df"
            }
          },
          "metadata": {},
          "execution_count": 58
        }
      ],
      "source": [
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 59,
      "metadata": {
        "id": "mKQfB2SWgLoM",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "65544888-ce69-4dc6-ec81-78361c3af146"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "54\n",
            "['frame.time_epoch', 'frame.time_delta', 'frame.time_delta_displayed', 'frame.time_relative', 'frame.len', 'frame.cap_len', 'radiotap.length', 'radiotap.present.rate', 'radiotap.present.dbm_antsignal', 'radiotap.present.antenna', 'radiotap.present.mcs', 'radiotap.present.ampdu', 'radiotap.present.rtap_ns', 'radiotap.present.ext', 'radiotap.mactime', 'radiotap.flags.preamble', 'radiotap.datarate', 'radiotap.channel.type.ofdm', 'radiotap.channel.type.dynamic', 'radiotap.dbm_antsignal', 'radiotap.antenna', 'wlan.fc.type_subtype', 'wlan.fc.type', 'wlan.fc.subtype', 'wlan.fc.ds', 'wlan.fc.retry', 'wlan.fc.pwrmgt', 'wlan.fc.moredata', 'wlan.fc.protected', 'wlan.duration', 'wlan.ra', 'wlan.da', 'wlan.ta', 'wlan.sa', 'wlan.bssid', 'wlan.frag', 'wlan.seq', 'wlan.ba.control.ackpolicy', 'wlan.ba.control.multitid', 'wlan.ba.control.cbitmap', 'wlan.ba.bm', 'wlan_mgt.fixed.fragment', 'wlan_mgt.fixed.sequence', 'wlan.wep.iv', 'wlan.wep.key', 'wlan.wep.icv', 'wlan.qos.tid', 'wlan.qos.priority', 'wlan.qos.ack', 'wlan.qos.amsdupresent', 'wlan.qos.bit4', 'wlan.qos.txop_dur_req', 'data.len', 'class']\n"
          ]
        }
      ],
      "source": [
        "column_names_list = df.columns.tolist() # Column count and name of the columns\n",
        "print(len(column_names_list))\n",
        "print(column_names_list)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 60,
      "metadata": {
        "id": "SehB3CyTgARD",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bf1cdc48-96fa-4e3b-8a78-abc342c08a6b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   frame.time_delta  frame.time_delta_displayed  frame.time_relative  \\\n",
            "0          0.000000                    0.000000             0.000000   \n",
            "1          0.000007                    0.000007             0.000007   \n",
            "2          0.000007                    0.000007             0.000014   \n",
            "3          0.000768                    0.000768             0.000782   \n",
            "4          0.000023                    0.000023             0.002247   \n",
            "\n",
            "   frame.len  frame.cap_len  radiotap.present.rate  \\\n",
            "0         52             52                      1   \n",
            "1        110            110                      1   \n",
            "2         52             52                      1   \n",
            "3        110            110                      1   \n",
            "4         52             52                      1   \n",
            "\n",
            "   radiotap.present.dbm_antsignal  radiotap.present.antenna  \\\n",
            "0                               1                         0   \n",
            "1                               1                         0   \n",
            "2                               1                         0   \n",
            "3                               1                         0   \n",
            "4                               1                         0   \n",
            "\n",
            "   radiotap.present.rtap_ns  radiotap.present.ext  ...  wlan.fc.subtype  \\\n",
            "0                         1                     1  ...               13   \n",
            "1                         1                     1  ...                0   \n",
            "2                         1                     1  ...               13   \n",
            "3                         1                     1  ...                0   \n",
            "4                         1                     1  ...               13   \n",
            "\n",
            "  wlan.fc.ds wlan.duration  wlan.frag  wlan.seq wlan.ba.control.ackpolicy  \\\n",
            "0       0x00             0        NaN       NaN                       NaN   \n",
            "1       0x01            48          0       717                       NaN   \n",
            "2       0x00             0        NaN       NaN                       NaN   \n",
            "3       0x02             0          0      3288                       NaN   \n",
            "4       0x00             0        NaN       NaN                       NaN   \n",
            "\n",
            "  wlan.qos.tid wlan.qos.priority data.len   class  \n",
            "0          NaN               NaN      NaN  normal  \n",
            "1          NaN               NaN       36  normal  \n",
            "2          NaN               NaN      NaN  normal  \n",
            "3          NaN               NaN       36  normal  \n",
            "4          NaN               NaN      NaN  normal  \n",
            "\n",
            "[5 rows x 24 columns]\n"
          ]
        }
      ],
      "source": [
        "# List of columns to drop\n",
        "columns_to_drop = [\n",
        "    'frame.time_epoch', 'radiotap.length', 'radiotap.present.mcs',\n",
        "    'radiotap.present.ampdu', 'radiotap.flags.preamble', 'radiotap.channel.type.ofdm',\n",
        "    'radiotap.channel.type.dynamic', 'radiotap.antenna', 'wlan.fc.retry',\n",
        "    'wlan.fc.pwrmgt', 'wlan.fc.moredata', 'wlan.fc.protected', 'wlan.ba.control.multitid',\n",
        "    'wlan.ba.control.cbitmap', 'wlan_mgt.fixed.fragment', 'wlan_mgt.fixed.sequence',\n",
        "    'wlan.qos.ack', 'wlan.qos.amsdupresent', 'wlan.qos.bit4', 'wlan.qos.txop_dur_req',\n",
        "    'wlan.wep.iv', 'wlan.wep.key', 'wlan.wep.icv','wlan.sa','wlan.ta', 'wlan.da','wlan.ra','wlan.bssid','wlan.ba.bm','radiotap.mactime']\n",
        "\n",
        "# Drop the columns from the DataFrame\n",
        "df.drop(columns=columns_to_drop, inplace=True)\n",
        "\n",
        "# Display the resulting DataFrame\n",
        "print(df.head())\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 61,
      "metadata": {
        "id": "E1gdTPHGgCV3"
      },
      "outputs": [],
      "source": [
        "df.replace(np.nan,0, inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 62,
      "metadata": {
        "id": "DVcl1eZOgC66",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        },
        "outputId": "f2e5bb18-341c-4f7c-fec9-7193f63f20b2"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   frame.time_delta  frame.time_delta_displayed  frame.time_relative  \\\n",
              "0          0.000000                    0.000000             0.000000   \n",
              "1          0.000007                    0.000007             0.000007   \n",
              "2          0.000007                    0.000007             0.000014   \n",
              "3          0.000768                    0.000768             0.000782   \n",
              "4          0.000023                    0.000023             0.002247   \n",
              "\n",
              "   frame.len  frame.cap_len  radiotap.present.rate  \\\n",
              "0         52             52                      1   \n",
              "1        110            110                      1   \n",
              "2         52             52                      1   \n",
              "3        110            110                      1   \n",
              "4         52             52                      1   \n",
              "\n",
              "   radiotap.present.dbm_antsignal  radiotap.present.antenna  \\\n",
              "0                               1                         0   \n",
              "1                               1                         0   \n",
              "2                               1                         0   \n",
              "3                               1                         0   \n",
              "4                               1                         0   \n",
              "\n",
              "   radiotap.present.rtap_ns  radiotap.present.ext  ...  wlan.fc.subtype  \\\n",
              "0                         1                     1  ...               13   \n",
              "1                         1                     1  ...                0   \n",
              "2                         1                     1  ...               13   \n",
              "3                         1                     1  ...                0   \n",
              "4                         1                     1  ...               13   \n",
              "\n",
              "  wlan.fc.ds wlan.duration  wlan.frag  wlan.seq wlan.ba.control.ackpolicy  \\\n",
              "0       0x00             0          0         0                         0   \n",
              "1       0x01            48          0       717                         0   \n",
              "2       0x00             0          0         0                         0   \n",
              "3       0x02             0          0      3288                         0   \n",
              "4       0x00             0          0         0                         0   \n",
              "\n",
              "  wlan.qos.tid wlan.qos.priority data.len   class  \n",
              "0            0                 0        0  normal  \n",
              "1            0                 0       36  normal  \n",
              "2            0                 0        0  normal  \n",
              "3            0                 0       36  normal  \n",
              "4            0                 0        0  normal  \n",
              "\n",
              "[5 rows x 24 columns]"
            ],
            "text/html": [
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              "  <div id=\"df-78b8e558-6efb-4319-a78e-b36a19e81766\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>frame.time_delta</th>\n",
              "      <th>frame.time_delta_displayed</th>\n",
              "      <th>frame.time_relative</th>\n",
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              "      <th>radiotap.present.dbm_antsignal</th>\n",
              "      <th>radiotap.present.antenna</th>\n",
              "      <th>radiotap.present.rtap_ns</th>\n",
              "      <th>radiotap.present.ext</th>\n",
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              "      <th>wlan.fc.subtype</th>\n",
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              "      <td>0</td>\n",
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              "      <td>36</td>\n",
              "      <td>normal</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
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              "      <td>0.000007</td>\n",
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              "      <td>52</td>\n",
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              "      <td>0</td>\n",
              "      <td>normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.000768</td>\n",
              "      <td>0.000768</td>\n",
              "      <td>0.000782</td>\n",
              "      <td>110</td>\n",
              "      <td>110</td>\n",
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              "      <td>36</td>\n",
              "      <td>normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.000023</td>\n",
              "      <td>0.000023</td>\n",
              "      <td>0.002247</td>\n",
              "      <td>52</td>\n",
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              "      <td>0</td>\n",
              "      <td>normal</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 24 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-78b8e558-6efb-4319-a78e-b36a19e81766')\"\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
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              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-78b8e558-6efb-4319-a78e-b36a19e81766 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-78b8e558-6efb-4319-a78e-b36a19e81766');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
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              "\n",
              "\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
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              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
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              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-5370e990-58cc-4731-9f37-f4576105c1e0 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df"
            }
          },
          "metadata": {},
          "execution_count": 62
        }
      ],
      "source": [
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 63,
      "metadata": {
        "id": "VmrJMeW1gOWI",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "71a0cac0-e7cc-4b74-d279-720a2ef3911a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "24\n",
            "['frame.time_delta', 'frame.time_delta_displayed', 'frame.time_relative', 'frame.len', 'frame.cap_len', 'radiotap.present.rate', 'radiotap.present.dbm_antsignal', 'radiotap.present.antenna', 'radiotap.present.rtap_ns', 'radiotap.present.ext', 'radiotap.datarate', 'radiotap.dbm_antsignal', 'wlan.fc.type_subtype', 'wlan.fc.type', 'wlan.fc.subtype', 'wlan.fc.ds', 'wlan.duration', 'wlan.frag', 'wlan.seq', 'wlan.ba.control.ackpolicy', 'wlan.qos.tid', 'wlan.qos.priority', 'data.len', 'class']\n"
          ]
        }
      ],
      "source": [
        "column_names_list = df.columns.tolist()\n",
        "print(len(column_names_list))\n",
        "print(column_names_list)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 64,
      "metadata": {
        "id": "wOIsGZ8IgdNG",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "844eca26-d00c-4728-bccd-7f7c27f9d9f3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Class counts in the DataFrame: {'normal': 12998132, 'evil_twin': 50643}\n",
            "normal: 12998132\n",
            "evil_twin: 50643\n"
          ]
        }
      ],
      "source": [
        "def unique_class_counts(df, target_column):\n",
        "    class_counts = df[target_column].value_counts().to_dict()\n",
        "    return class_counts\n",
        "\n",
        "# Get unique class counts\n",
        "class_counts = unique_class_counts(df, 'class')\n",
        "print(f\"Class counts in the DataFrame: {class_counts}\")\n",
        "for class_name, count in class_counts.items():\n",
        "    print(f\"{class_name}: {count}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 65,
      "metadata": {
        "id": "n0_Ykjdage66"
      },
      "outputs": [],
      "source": [
        "# Filter rows where the 'class' column is equal to 'normal' (extracting normal class data)\n",
        "df1 = df.loc[df['class'] == 'normal']\n",
        "\n",
        "# Randomly sample 2% of the rows from the normal class data without replacement\n",
        "df2 = df1.sample(frac=0.02, replace=False, random_state=None, axis=0)\n",
        "\n",
        "# Filter rows where the 'class' column is NOT equal to 'normal' (all other class data)\n",
        "df3 = df.loc[df['class'] != 'normal']\n",
        "\n",
        "# Combine the sampled 'normal' class rows (df2) with all rows from other classes (df3)\n",
        "df4 = pd.concat([df2, df3])\n",
        "\n",
        "# Update the original dataframe to include only the reduced normal class data and all other classes\n",
        "df = df4\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 66,
      "metadata": {
        "id": "7cwNUFkHggSo",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "207c780b-571d-4413-860e-97f55d1df632"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Class counts in the DataFrame: {'normal': 259963, 'evil_twin': 50643}\n",
            "normal: 259963\n",
            "evil_twin: 50643\n"
          ]
        }
      ],
      "source": [
        "def unique_class_counts(df, target_column):\n",
        "    class_counts = df[target_column].value_counts().to_dict()\n",
        "    return class_counts\n",
        "\n",
        "# Get unique class counts\n",
        "class_counts = unique_class_counts(df, 'class')\n",
        "print(f\"Class counts in the DataFrame: {class_counts}\")\n",
        "for class_name, count in class_counts.items():\n",
        "    print(f\"{class_name}: {count}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 67,
      "metadata": {
        "id": "kbSNwTPCgiel"
      },
      "outputs": [],
      "source": [
        "# Convert hexadecimal strings to numeric (integer) values\n",
        "df['wlan.fc.type_subtype'] = df['wlan.fc.type_subtype'].apply(lambda x: int(x, 16))\n",
        "df['wlan.fc.ds'] = df['wlan.fc.ds'].apply(lambda x: int(x, 16))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 68,
      "metadata": {
        "id": "4l9D1KxDg6Qu"
      },
      "outputs": [],
      "source": [
        "df_normalized = df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 69,
      "metadata": {
        "id": "4GpLxms2g72F"
      },
      "outputs": [],
      "source": [
        "# Assuming df is your original DataFrame with columns including 'class'\n",
        "# Load or define your DataFrame, adjust the filename or method accordingly\n",
        "\n",
        "# 1. Filter evil_twin instances\n",
        "df_evil_twin = df_normalized[df_normalized['class'] == 'evil_twin']\n",
        "\n",
        "# 2. Sample 60,000 normal instances randomly\n",
        "df_normal = df_normalized[df_normalized['class'] == 'normal'].sample(n=60000, random_state=42)\n",
        "\n",
        "# 3. Concatenate both DataFrames\n",
        "df_normalized = pd.concat([df_evil_twin, df_normal], ignore_index=True)\n",
        "\n",
        "# Optionally, you can shuffle the DataFrame if needed\n",
        "df_normalized = df_normalized.sample(frac=1, random_state=42).reset_index(drop=True)\n",
        "\n",
        "# Print the shape to verify\n",
        "#print(\"Shape of df_normalized:\", df_normalized.shape)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 70,
      "metadata": {
        "id": "HmW1jJGLhDPo",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "415a288d-d4ff-44e8-aee4-e24b3b527652"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Class counts in the DataFrame: {'normal': 60000, 'evil_twin': 50643}\n",
            "normal: 60000\n",
            "evil_twin: 50643\n"
          ]
        }
      ],
      "source": [
        "def unique_class_counts(df_normalized, target_column):\n",
        "    class_counts = df_normalized[target_column].value_counts().to_dict()\n",
        "    return class_counts\n",
        "\n",
        "# Get unique class counts\n",
        "class_counts = unique_class_counts(df_normalized, 'class')\n",
        "print(f\"Class counts in the DataFrame: {class_counts}\")\n",
        "for class_name, count in class_counts.items():\n",
        "    print(f\"{class_name}: {count}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 71,
      "metadata": {
        "id": "Mz66Wi62hG4C"
      },
      "outputs": [],
      "source": [
        "from sklearn.utils import shuffle\n",
        "\n",
        "df_normalized = shuffle(df_normalized)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 72,
      "metadata": {
        "id": "5AVAeirchKzh"
      },
      "outputs": [],
      "source": [
        "df = df_normalized"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EPKmAfZ9b56O"
      },
      "source": [
        "### 2.2.2 Random Forest Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 73,
      "metadata": {
        "id": "U9cFTpHlcM-t",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e4598426-26be-48c4-d869-5688b857a76f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best Hyperparameters: {'n_estimators': 150, 'min_samples_split': 4, 'min_samples_leaf': 1, 'max_features': 'sqrt', 'max_depth': 20, 'criterion': 'gini'}\n",
            "Accuracy: 0.9990962085950562\n",
            "Accuracy: 0.9985 for parameters: {'n_estimators': 100, 'min_samples_split': 6, 'min_samples_leaf': 6, 'max_features': 'log2', 'max_depth': 5, 'criterion': 'entropy'}\n",
            "Accuracy: 0.9992 for parameters: {'n_estimators': 5, 'min_samples_split': 7, 'min_samples_leaf': 2, 'max_features': None, 'max_depth': None, 'criterion': 'gini'}\n",
            "Accuracy: 0.9990 for parameters: {'n_estimators': 5, 'min_samples_split': 10, 'min_samples_leaf': 6, 'max_features': None, 'max_depth': 4, 'criterion': 'gini'}\n",
            "Accuracy: 0.9786 for parameters: {'n_estimators': 50, 'min_samples_split': 10, 'min_samples_leaf': 1, 'max_features': 'sqrt', 'max_depth': 2, 'criterion': 'entropy'}\n",
            "Accuracy: 0.9783 for parameters: {'n_estimators': 50, 'min_samples_split': 5, 'min_samples_leaf': 2, 'max_features': 'log2', 'max_depth': 1, 'criterion': 'entropy'}\n",
            "Accuracy: 0.9992 for parameters: {'n_estimators': 100, 'min_samples_split': 7, 'min_samples_leaf': 2, 'max_features': None, 'max_depth': 5, 'criterion': 'gini'}\n",
            "Accuracy: 0.9991 for parameters: {'n_estimators': 5, 'min_samples_split': 4, 'min_samples_leaf': 6, 'max_features': 'log2', 'max_depth': 10, 'criterion': 'entropy'}\n",
            "Accuracy: 0.9973 for parameters: {'n_estimators': 150, 'min_samples_split': 3, 'min_samples_leaf': 2, 'max_features': 'sqrt', 'max_depth': 4, 'criterion': 'gini'}\n",
            "Accuracy: 0.9978 for parameters: {'n_estimators': 25, 'min_samples_split': 4, 'min_samples_leaf': 1, 'max_features': 'log2', 'max_depth': 4, 'criterion': 'entropy'}\n",
            "Accuracy: 0.9783 for parameters: {'n_estimators': 25, 'min_samples_split': 4, 'min_samples_leaf': 4, 'max_features': 'log2', 'max_depth': 1, 'criterion': 'entropy'}\n",
            "Accuracy: 0.9991 for parameters: {'n_estimators': 25, 'min_samples_split': 2, 'min_samples_leaf': 4, 'max_features': None, 'max_depth': 5, 'criterion': 'entropy'}\n",
            "Accuracy: 0.9992 for parameters: {'n_estimators': 20, 'min_samples_split': 6, 'min_samples_leaf': 1, 'max_features': None, 'max_depth': 15, 'criterion': 'gini'}\n",
            "Accuracy: 0.9784 for parameters: {'n_estimators': 100, 'min_samples_split': 8, 'min_samples_leaf': 4, 'max_features': 'log2', 'max_depth': 1, 'criterion': 'gini'}\n",
            "Accuracy: 0.9992 for parameters: {'n_estimators': 300, 'min_samples_split': 6, 'min_samples_leaf': 1, 'max_features': None, 'max_depth': None, 'criterion': 'gini'}\n",
            "Accuracy: 0.9786 for parameters: {'n_estimators': 25, 'min_samples_split': 2, 'min_samples_leaf': 6, 'max_features': 'sqrt', 'max_depth': 2, 'criterion': 'gini'}\n",
            "Accuracy: 0.9990 for parameters: {'n_estimators': 10, 'min_samples_split': 2, 'min_samples_leaf': 6, 'max_features': None, 'max_depth': 4, 'criterion': 'gini'}\n",
            "Accuracy: 0.9990 for parameters: {'n_estimators': 10, 'min_samples_split': 3, 'min_samples_leaf': 1, 'max_features': None, 'max_depth': 4, 'criterion': 'gini'}\n",
            "Accuracy: 0.9991 for parameters: {'n_estimators': 5, 'min_samples_split': 5, 'min_samples_leaf': 6, 'max_features': None, 'max_depth': 5, 'criterion': 'gini'}\n",
            "Accuracy: 0.9991 for parameters: {'n_estimators': 250, 'min_samples_split': 3, 'min_samples_leaf': 6, 'max_features': 'sqrt', 'max_depth': 15, 'criterion': 'entropy'}\n",
            "Accuracy: 0.9991 for parameters: {'n_estimators': 150, 'min_samples_split': 2, 'min_samples_leaf': 6, 'max_features': None, 'max_depth': 5, 'criterion': 'gini'}\n",
            "Accuracy: 0.9992 for parameters: {'n_estimators': 150, 'min_samples_split': 4, 'min_samples_leaf': 1, 'max_features': 'sqrt', 'max_depth': 20, 'criterion': 'gini'}\n",
            "Accuracy: 0.9992 for parameters: {'n_estimators': 20, 'min_samples_split': 6, 'min_samples_leaf': 4, 'max_features': 'log2', 'max_depth': 20, 'criterion': 'entropy'}\n",
            "Accuracy: 0.9992 for parameters: {'n_estimators': 10, 'min_samples_split': 9, 'min_samples_leaf': 1, 'max_features': 'sqrt', 'max_depth': 20, 'criterion': 'entropy'}\n",
            "Accuracy: 0.9786 for parameters: {'n_estimators': 200, 'min_samples_split': 6, 'min_samples_leaf': 4, 'max_features': 'log2', 'max_depth': 2, 'criterion': 'gini'}\n",
            "Accuracy: 0.9992 for parameters: {'n_estimators': 300, 'min_samples_split': 2, 'min_samples_leaf': 4, 'max_features': 'sqrt', 'max_depth': None, 'criterion': 'gini'}\n",
            "Accuracy: 0.9992 for parameters: {'n_estimators': 250, 'min_samples_split': 5, 'min_samples_leaf': 1, 'max_features': None, 'max_depth': 20, 'criterion': 'entropy'}\n",
            "Accuracy: 0.9960 for parameters: {'n_estimators': 100, 'min_samples_split': 5, 'min_samples_leaf': 2, 'max_features': None, 'max_depth': 2, 'criterion': 'gini'}\n",
            "Accuracy: 0.9785 for parameters: {'n_estimators': 300, 'min_samples_split': 2, 'min_samples_leaf': 2, 'max_features': None, 'max_depth': 1, 'criterion': 'gini'}\n",
            "Accuracy: 0.9784 for parameters: {'n_estimators': 200, 'min_samples_split': 2, 'min_samples_leaf': 4, 'max_features': 'sqrt', 'max_depth': 1, 'criterion': 'gini'}\n",
            "Accuracy: 0.9991 for parameters: {'n_estimators': 20, 'min_samples_split': 2, 'min_samples_leaf': 6, 'max_features': 'log2', 'max_depth': 15, 'criterion': 'entropy'}\n",
            "Accuracy: 0.9783 for parameters: {'n_estimators': 50, 'min_samples_split': 7, 'min_samples_leaf': 4, 'max_features': 'log2', 'max_depth': 1, 'criterion': 'entropy'}\n",
            "Accuracy: 0.9856 for parameters: {'n_estimators': 150, 'min_samples_split': 6, 'min_samples_leaf': 6, 'max_features': 'sqrt', 'max_depth': 3, 'criterion': 'gini'}\n",
            "Accuracy: 0.9824 for parameters: {'n_estimators': 5, 'min_samples_split': 4, 'min_samples_leaf': 6, 'max_features': 'log2', 'max_depth': 3, 'criterion': 'gini'}\n",
            "Accuracy: 0.9992 for parameters: {'n_estimators': 250, 'min_samples_split': 3, 'min_samples_leaf': 4, 'max_features': 'sqrt', 'max_depth': 20, 'criterion': 'gini'}\n",
            "Accuracy: 0.9979 for parameters: {'n_estimators': 20, 'min_samples_split': 3, 'min_samples_leaf': 2, 'max_features': 'log2', 'max_depth': 4, 'criterion': 'entropy'}\n",
            "Accuracy: 0.9991 for parameters: {'n_estimators': 200, 'min_samples_split': 2, 'min_samples_leaf': 4, 'max_features': None, 'max_depth': None, 'criterion': 'entropy'}\n",
            "Accuracy: 0.9992 for parameters: {'n_estimators': 20, 'min_samples_split': 8, 'min_samples_leaf': 2, 'max_features': 'sqrt', 'max_depth': 20, 'criterion': 'gini'}\n",
            "Accuracy: 0.9992 for parameters: {'n_estimators': 100, 'min_samples_split': 4, 'min_samples_leaf': 2, 'max_features': None, 'max_depth': None, 'criterion': 'entropy'}\n",
            "Accuracy: 0.9992 for parameters: {'n_estimators': 25, 'min_samples_split': 10, 'min_samples_leaf': 2, 'max_features': 'log2', 'max_depth': None, 'criterion': 'entropy'}\n",
            "Accuracy: 0.9992 for parameters: {'n_estimators': 25, 'min_samples_split': 8, 'min_samples_leaf': 2, 'max_features': None, 'max_depth': 15, 'criterion': 'gini'}\n",
            "Accuracy: 0.9992 for parameters: {'n_estimators': 25, 'min_samples_split': 8, 'min_samples_leaf': 2, 'max_features': 'log2', 'max_depth': None, 'criterion': 'entropy'}\n",
            "Accuracy: 0.9784 for parameters: {'n_estimators': 150, 'min_samples_split': 5, 'min_samples_leaf': 4, 'max_features': 'sqrt', 'max_depth': 1, 'criterion': 'entropy'}\n",
            "Accuracy: 0.9786 for parameters: {'n_estimators': 20, 'min_samples_split': 2, 'min_samples_leaf': 2, 'max_features': 'log2', 'max_depth': 2, 'criterion': 'gini'}\n",
            "Accuracy: 0.9985 for parameters: {'n_estimators': 200, 'min_samples_split': 5, 'min_samples_leaf': 6, 'max_features': 'log2', 'max_depth': 5, 'criterion': 'entropy'}\n",
            "Accuracy: 0.9991 for parameters: {'n_estimators': 300, 'min_samples_split': 7, 'min_samples_leaf': 4, 'max_features': None, 'max_depth': 10, 'criterion': 'entropy'}\n",
            "Accuracy: 0.9786 for parameters: {'n_estimators': 150, 'min_samples_split': 7, 'min_samples_leaf': 6, 'max_features': 'sqrt', 'max_depth': 2, 'criterion': 'entropy'}\n",
            "Accuracy: 0.9992 for parameters: {'n_estimators': 150, 'min_samples_split': 5, 'min_samples_leaf': 6, 'max_features': 'log2', 'max_depth': 15, 'criterion': 'gini'}\n",
            "Accuracy: 0.9985 for parameters: {'n_estimators': 10, 'min_samples_split': 7, 'min_samples_leaf': 2, 'max_features': 'log2', 'max_depth': 4, 'criterion': 'entropy'}\n",
            "Accuracy: 0.9960 for parameters: {'n_estimators': 20, 'min_samples_split': 2, 'min_samples_leaf': 6, 'max_features': None, 'max_depth': 2, 'criterion': 'gini'}\n",
            "Accuracy: 0.9784 for parameters: {'n_estimators': 100, 'min_samples_split': 7, 'min_samples_leaf': 2, 'max_features': 'log2', 'max_depth': 1, 'criterion': 'gini'}\n",
            "Best parameters: {'n_estimators': 150, 'min_samples_split': 4, 'min_samples_leaf': 1, 'max_features': 'sqrt', 'max_depth': 20, 'criterion': 'gini'} with accuracy: 0.9992\n"
          ]
        }
      ],
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.model_selection import train_test_split, RandomizedSearchCV\n",
        "from sklearn.metrics import accuracy_score\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "# Assuming df is your DataFrame\n",
        "# df = pd.read_csv('your_data.csv') # Replace with your actual data loading method\n",
        "\n",
        "# Split the data into features (X) and target variable (y)\n",
        "X = df_normalized.drop(columns=['class'])\n",
        "y = df_normalized['class']\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
        "\n",
        "# Hyperparameters to tune\n",
        "param_dist = {\n",
        "    'n_estimators':[5, 10, 20, 25,  50, 100, 150, 200, 250, 300],\n",
        "    'max_depth': [None, 1, 2, 3, 4, 5, 10, 15, 20],\n",
        "    'min_samples_split': [2, 3,4 ,5 ,6 ,7 , 8, 9, 10],\n",
        "    'min_samples_leaf': [1, 2, 4,6],\n",
        "    'criterion': ['gini', 'entropy'],\n",
        "    'max_features': ['sqrt', 'log2', None]  # Set max_features explicitly\n",
        "}\n",
        "\n",
        "# Create a Random Forest classifier\n",
        "rf_classifier = RandomForestClassifier(random_state=42)\n",
        "\n",
        "# Perform randomized search to find the best hyperparameters\n",
        "random_search = RandomizedSearchCV(estimator=rf_classifier, param_distributions=param_dist,\n",
        "                                   n_iter=50, cv=5, scoring='accuracy', n_jobs=-1, random_state=42)\n",
        "random_search.fit(X_train, y_train)\n",
        "\n",
        "# Get the best hyperparameters\n",
        "best_params = random_search.best_params_\n",
        "print(\"Best Hyperparameters:\", best_params)\n",
        "\n",
        "# Train the Random Forest classifier with the best hyperparameters\n",
        "best_rf_classifier = RandomForestClassifier(random_state=42, **best_params)\n",
        "best_rf_classifier.fit(X_train, y_train)\n",
        "\n",
        "# Make predictions on the testing data\n",
        "y_pred = best_rf_classifier.predict(X_test)\n",
        "\n",
        "# Calculate accuracy\n",
        "accuracy = accuracy_score(y_test, y_pred)\n",
        "print(\"Accuracy:\", accuracy)\n",
        "\n",
        "# Show accuracy for each parameter combination\n",
        "results = random_search.cv_results_\n",
        "for mean_score, params in zip(results['mean_test_score'], results['params']):\n",
        "    print(f\"Accuracy: {mean_score:.4f} for parameters: {params}\")\n",
        "\n",
        "# Print the best parameters and their corresponding accuracy\n",
        "best_index = np.argmax(results['mean_test_score'])\n",
        "print(f\"Best parameters: {results['params'][best_index]} with accuracy: {results['mean_test_score'][best_index]:.4f}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 74,
      "metadata": {
        "id": "PMzL_5aEhxew",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "148be475-7c9c-4e8a-bf27-455fa0945a32"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Random Forest Model Matrices - Training Metrics:\n",
            "Accuracy: 0.9992204622997491\n",
            "Recall: 0.9992204622997491\n",
            "F1-score: 0.9992205142954689\n",
            "Precision: 0.9992217893524808\n",
            "\n",
            "Random Forest Model Matrices -Testing Metrics:\n",
            "Accuracy: 0.9990962085950562\n",
            "Recall: 0.9990962085950562\n",
            "F1-score: 0.9990962735610198\n",
            "Precision: 0.9990979807350658\n"
          ]
        }
      ],
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.metrics import accuracy_score, recall_score, f1_score, precision_score\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "\n",
        "X = df_normalized.drop(columns=['class'])\n",
        "y = df_normalized['class']\n",
        "\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) #statify=y\n",
        "# Train the Random Forest classifier\n",
        "classifier = RandomForestClassifier( random_state=42,n_estimators=100, min_samples_split=6, min_samples_leaf=2, max_features='sqrt', max_depth=None, criterion= 'gini')\n",
        "classifier.fit(X_train, y_train)\n",
        "\n",
        "\n",
        "y_train_pred = classifier.predict(X_train)\n",
        "y_test_pred = classifier.predict(X_test)\n",
        "\n",
        "\n",
        "\n",
        "# Assuming y_train_pred and y_test_pred are predictions made by the Random Forest model\n",
        "train_accuracy = accuracy_score(y_train, y_train_pred)\n",
        "train_recall = recall_score(y_train, y_train_pred, average='weighted')\n",
        "train_f1 = f1_score(y_train, y_train_pred, average='weighted')\n",
        "train_precision = precision_score(y_train, y_train_pred, average='weighted')\n",
        "\n",
        "test_accuracy = accuracy_score(y_test, y_test_pred)\n",
        "test_recall = recall_score(y_test, y_test_pred, average='weighted')\n",
        "test_f1 = f1_score(y_test, y_test_pred, average='weighted')\n",
        "test_precision = precision_score(y_test, y_test_pred, average='weighted')\n",
        "\n",
        "print(\"Random Forest Model Matrices - Training Metrics:\")\n",
        "print(\"Accuracy:\", train_accuracy)\n",
        "print(\"Recall:\", train_recall)\n",
        "print(\"F1-score:\", train_f1)\n",
        "print(\"Precision:\", train_precision)\n",
        "\n",
        "print(\"\\nRandom Forest Model Matrices -Testing Metrics:\")\n",
        "print(\"Accuracy:\", test_accuracy)\n",
        "print(\"Recall:\", test_recall)\n",
        "print(\"F1-score:\", test_f1)\n",
        "print(\"Precision:\", test_precision)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 75,
      "metadata": {
        "id": "XJs54M9Rh1pV",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "47e8f1e7-aad0-4191-8602-3996c4c1c868"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "   evil_twin       1.00      1.00      1.00     10180\n",
            "      normal       1.00      1.00      1.00     11949\n",
            "\n",
            "    accuracy                           1.00     22129\n",
            "   macro avg       1.00      1.00      1.00     22129\n",
            "weighted avg       1.00      1.00      1.00     22129\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# prompt: classification report\n",
        "\n",
        "from sklearn.metrics import classification_report\n",
        "\n",
        "\n",
        "print(classification_report(y_test, y_test_pred))\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 76,
      "metadata": {
        "id": "-AZBCyRMh9_3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9e3e571e-15b8-42ec-834b-95de955e012f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cross-Validation Scores: [0.99909621 0.99927697 0.99909621 0.99923174 0.99923174]\n",
            "Mean Cross-Validation Score: 0.9991865738486616\n"
          ]
        }
      ],
      "source": [
        "from sklearn.model_selection import cross_val_score\n",
        "\n",
        "\n",
        "cv_scores = cross_val_score(classifier, X, y, cv=5)\n",
        "\n",
        "\n",
        "print(\"Cross-Validation Scores:\", cv_scores)\n",
        "\n",
        "\n",
        "mean_cv_score = cv_scores.mean()\n",
        "print(\"Mean Cross-Validation Score:\", mean_cv_score)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 77,
      "metadata": {
        "id": "hbV_1MBahUEH",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "outputId": "dd17818d-755c-4a4c-a44a-6d661555ea79"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Compute confusion matrix\n",
        "cm = confusion_matrix(y_test, y_test_pred)\n",
        "\n",
        "# Plot confusion matrix\n",
        "plt.figure(figsize=(8, 6))\n",
        "sns.heatmap(cm, annot=True, cmap='Blues', fmt='g',\n",
        "            xticklabels=['normal', 'evil_twin'],\n",
        "            yticklabels=['normal', 'evil_twin'])\n",
        "plt.xlabel('Predicted')\n",
        "plt.ylabel('Actual')\n",
        "plt.title('Confusion Matrix - Random Forest Model')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 78,
      "metadata": {
        "id": "SIJeFk-giINW",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "c5495623-85c0-4f54-ee51-f9a1e11caab2"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.model_selection import learning_curve\n",
        "\n",
        "def plot_learning_curve_modified(estimator, title, X, y, ylim=None, cv=None,\n",
        "                                 n_jobs=-1, train_sizes=np.linspace(.1, 1.0, 5)):\n",
        "    plt.figure(figsize=(10, 6))  # Adjust the figure size\n",
        "    plt.title(title, fontsize=16)  # Set the title and adjust fontsize\n",
        "    if ylim is not None:\n",
        "        plt.ylim(*ylim)\n",
        "    plt.xlabel(\"Training examples\", fontsize=14)  # Set xlabel and adjust fontsize\n",
        "    plt.ylabel(\"Score\", fontsize=14)  # Set ylabel and adjust fontsize\n",
        "    train_sizes, train_scores, test_scores = learning_curve(\n",
        "        estimator, X, y, cv=cv, n_jobs=n_jobs, train_sizes=train_sizes)\n",
        "    train_scores_mean = np.mean(train_scores, axis=1)\n",
        "    train_scores_std = np.std(train_scores, axis=1)\n",
        "    test_scores_mean = np.mean(test_scores, axis=1)\n",
        "    test_scores_std = np.std(test_scores, axis=1)\n",
        "    plt.grid()\n",
        "\n",
        "    plt.fill_between(train_sizes, train_scores_mean - train_scores_std,\n",
        "                     train_scores_mean + train_scores_std, alpha=0.1,\n",
        "                     color=\"r\")\n",
        "    plt.fill_between(train_sizes, test_scores_mean - test_scores_std,\n",
        "                     test_scores_mean + test_scores_std, alpha=0.1, color=\"g\")\n",
        "    plt.plot(train_sizes, train_scores_mean, 'o-', color=\"r\",\n",
        "             label=\"Training score\")\n",
        "    plt.plot(train_sizes, test_scores_mean, 'o-', color=\"g\",\n",
        "             label=\"Cross-validation score\")\n",
        "\n",
        "    plt.legend(loc=\"best\", fontsize=12)  # Adjust the legend fontsize\n",
        "    plt.tick_params(labelsize=12)  # Adjust tick label fontsize\n",
        "    return plt\n",
        "\n",
        "# Plot learning curves for training set\n",
        "title = \"Learning Curves - Training Set\"\n",
        "plot_learning_curve_modified(classifier, title, X_train, y_train, cv=5)\n",
        "plt.show()\n",
        "\n",
        "# Plot learning curves for testing set\n",
        "title = \"Learning Curves - Testing Set\"\n",
        "plot_learning_curve_modified(classifier, title, X_test, y_test, cv=5)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 79,
      "metadata": {
        "id": "BQsmLL7PiLWJ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 749
        },
        "outputId": "4317ddad-2b48-471e-e99d-984593d2189c"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x800 with 1 Axes>"
            ],
            "image/png": 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TPYY/ePCg419Xb3X69GkzatQoU6pUKZMtWzbTtm1bU69ePZM5c2Yzbtw4u+Mlq0CBAmb9+vXGGGNKlixpFi5caIwxZv/+/Y7/oO1OcPqeDR599FHNnTtXw4YNk3Rz2GlCQoJGjx6txx9/3OZ0qatfv74mTJig6dOnS7qZ/cqVKxo0aJBXnCZ09epVZc2aNcn2CxcuyM/Pz4ZE6TNs2DANHDhQc+bMSTa/kxUuXFgzZszQ6NGjPbbPnDnT8ZM9lipVSgcOHFBISIgqVKig999/XyEhIZo2bZoKFChgd7xUrVu3TqtWrdKyZctUpkyZJMOUP/30U5uSpU/p0qU1cuRIzZw50z3nQlxcnEaOHOk1E25LN4e+336qhFPneZFunrLaunVrBQQEqGjRoqpTp46km6f1lStXzt5waahataqWLFmibt26SfrfKR0zZ850n3bgVMHBwTp8+LBCQkI8tq9bt07FihWzJ1Q6TZgwQf369XM/P3qT4sWLa8CAAdq0aZPKlSuX5HnylVdesSlZxnjbYhwDBw5Uu3bt9PPPPyshIUGffvqpDhw4oLlz5+r//u//7I6Xqty5c+urr75Sz549PbZ/9dVXyp07t6Sbx5rZs2e3I16Kjh07JmOMihUrpi1btihv3rzu63x9fZUvXz5lypTJxoRpi4+Pd/9eg4KCdPr0aZUqVUpFixbVgQMHbE6Xsm7duumjjz6SMUZt2rTR6NGjVbZsWff12bJl0zvvvKOCBQvamDJ5WbJk0fr161WiRAmP7evXr5e/v79NqdLn+vXr+vLLLzV79mx98803Kl++vHr06KFWrVq5j8M+++wzdejQIcnfsxM0b95crVq1UokSJXT+/Hk1bNhQkrRjxw4VL17c5nR/P0opG4wePVr16tXT1q1bFRcXp9dee0179uzRhQsXtH79ervjpWrs2LGKiIhQWFiY/vzzT7Vq1UqHDh1SUFCQPvroI7vjpclbC8GxY8fqyJEjyp8/v0JCQpIcODt5osHx48frX//6l5YtW6bq1atLujn/wqFDh7R48WKb06Wue/fu+uWXXyRJgwYNUoMGDfThhx/K19dXUVFR9oZLQ86cOdWsWTO7Y9yxadOm6emnn9b999/vfmP1ww8/yOVy6auvvrI5XequXr2qvn37auHChTp//nyS650674UkvfTSS6pWrZpOnTqlJ554Qj4+PpKkYsWKafjw4TanS92IESPUsGFD7d27Vzdu3NDEiRO1d+9ebdiwQWvWrLE7Xqo6d+6s7t27a9asWXK5XDp9+rQ2btyo3r17a8CAAXbHS1WLFi107do1PfDAA8qaNWuS1ycnz6E2ffp0BQQEaM2aNUkeIy6Xy/GllLcuxtGkSRN99dVXGjp0qLJly6aBAweqcuXK+uqrr/TEE0/YHS9VAwYMUJcuXbRq1Sr3nFLff/+9li5d6l7sZ8WKFapdu7adMZMoWrSoJCVZKOdWxuGL/pQtW1a7du1SaGioqlevrtGjR8vX11fTp093dHm/d+9evfvuu2revHmKH4AHBQV5zMfqFD169FCXLl20fft29+N98+bNmjVrluNfmwoUKKCEhAQ999xz2rJliypWrJhkn8cff9yxq9mNHz9eISEhOnXqlEaPHu1e4f6XX35x/Hxed8JlzG3LpMASly5d0uTJk7Vr1y5duXJFlStX1ssvv+z40ReSdOPGDX388cf64Ycf3Nlbt26tLFmy2B0tTbt371a9evVUuXJlrVy5Uo0bN/YoBB944AG7IyZryJAhqV4/aNAgi5LcmVOnTmnq1KnuCUxLly6tF1980fEjpW537do17d+/X0WKFFFQUJDdce55V69e1YcffujxuGnVqpWyZctmc7LUvfzyy1q1apWGDRumNm3aaMqUKfr555/1/vvva9SoUWrdurXdEe9ZR44c0ahRozxeW/v27ev4UV7GGI0YMUIjR47UtWvXJN1cRrx3797uD1GcKnG1w5S0a9fOoiT/PK1bt9aJEyc0YcKEZBfjaNSokd0R70nr16/X5MmT3aNzSpUqpW7duqlmzZo2J0tb+/btNWXKlCSvo8ePH1ebNm0cPdn5119/ratXr6p58+Y6fPiwnnrqKR08eFB58uTRggULVLduXbsj3pMWLlyoiRMnat++fZJuHot1795dzz77rM3JUjdv3jw988wzjh/RhZsopfCP482FIKw1dOhQ9e7dO8kpk3/88YfGjBnjyOXCb3Xjxg2tXr1aR44cUatWrZQ9e3adPn1agYGB7k9c8PcrUqSI5s6dqzp16igwMFDbt29X8eLFNW/ePH300UeOW0mtV69e6d533LhxdzEJ4uLidPjwYV25ckVhYWH8nVrI3LaSlzcoUKCAvvjiC1WrVk2BgYHaunWrSpYsqS+//FKjR4/WunXr7I6YrE6dOun55593nx4M61SqVEmXL1/WBx984D6tec6cOXrllVdUt25dffbZZzYnzJgLFy5kaPVbO4wcOVL58+dXhw4dPLbPmjVLZ8+e9arVP71Jhw4dNHHixCSn0l69elXdunXTrFmzbEqWfocOHdKqVav066+/Jhnl6PT3IBlFKWWT3377Tf/973/drXNYWJgiIyPd56M72YEDB/Tuu+96NOZdu3bVgw8+aHOytJ08eVKFCxdO9sXr5MmTKlKkiA2p0ufixYtatGiRjhw5oj59+ih37tzavn278ufPr0KFCtkdL1Vr167V+++/r6NHj+qTTz5RoUKFNG/ePIWGhqpWrVp2x0tRpkyZ9Msvvyhfvnwe28+fP698+fI59tQISTpx4oQaNGigkydPKjY2VgcPHlSxYsXUvXt3xcbGuk8zcLJ58+a5HzcbN25U0aJFNX78eBUrVkxNmjSxO16KAgICtHfvXhUpUkT333+/Pv30U1WrVk3Hjh1TuXLldOXKFbsjerj91OXt27frxo0bKlWqlCTp4MGDypQpk6pUqaKVK1faETHd4uPj9dlnn3m8tjZp0kSZMzt7tgJvP3g+cuSIZs+erSNHjmjixInKly+fli1bpiJFiqhMmTJ2x0vV3LlzNWbMGB06dEiSVLJkSfXp00dt2rSxOVnaAgMD9cMPPygkJERFixbV/Pnz9cgjj+jYsWMqU6aMe9Sd0zRp0kRff/218ubNq5YtW6p169bJnlrjdN42Z6B0c56d119/XZMmTdKrr76qw4cPa9myZRo3bpw6d+5sd7x7UkhIiObPn59kJN3mzZvVsmVLHTt2zKZk97aUjuHPnTun4OBg3bhxw6Zk6TNjxgx16dJFQUFBCg4O9njv6nK5HD11yx2xaYL1f7Q1a9aYwMBAU7hwYdOsWTPTrFkzU6RIERMYGGjWrFljd7xULVq0yL1qRM+ePU3Pnj1NjRo1TObMmc2iRYvsjpcmHx8fExMTk2T7uXPnjI+Pjw2J0mfXrl0mb968pnjx4iZz5szulWreeOMN06ZNG5vTpW7RokUmS5YsplOnTsbPz8+d/d133zUNGza0OV3qXC6X+fXXX5Nsj46ONkFBQTYkSr8mTZqY559/3sTGxnqsbrRq1SpTvHhxm9Ol7b333jNBQUFm+PDhxt/f351/9uzZpk6dOjanS125cuXcy7HXq1fPvPrqq8YYYyZOnGgKFSpkZ7Q0jR071jz99NPmwoUL7m0XLlwwTZo0Me+8846NydK2e/duU6xYMZM1a1ZTqVIlU6lSJZMtWzYTEhJifvzxR7vjpSql16azZ8+aTJky2ZAo/VavXm2yZMliwsPDja+vr/tvdeTIkeZf//qXzelSN3bsWJM1a1bz2muvmS+++MJ88cUXpk+fPiZr1qyOXZHpVlWrVjXLly83xhjz9NNPmzZt2piffvrJvPbaa6ZYsWI2p0vdhQsXzPvvv29q165tfHx8TFhYmHnrrbfcq1A61dWrV83LL79s8ubN617N7taLtxg4cKBxuVzmvvvuMxs2bLA7TrpcuXLFvPnmm6ZGjRrmgQceMKGhoR4Xp/Lz8zNHjx5Nst2pq6vmypXLnD171hhzc9X1XLlypXhxokuXLpmLFy8al8tlDh8+bC5duuS+XLhwwcyZM8cUKFDA7phpKlKkiBk1apTdMSxDKWWDsmXLms6dO5sbN264t924ccO88MILpmzZsjYmS1uxYsXMgAEDkmwfOHCg4w+AjEm5ZDh+/Ljjlu+9Vb169UyfPn2MMZ7LJ69fv94ULVrUxmRpq1ixopkzZ44xxjP79u3bTf78+e2MlqLEF2EfH58kL8iBgYHGx8fHvPTSS3bHTFXu3LndyyXf+ns/duyYyZIli53R0qV06dLms88+M8Z45v/xxx9Nnjx5bEyWtnHjxpmJEycaY4xZsWKF8ff3N35+fsbHx8dMmDDB5nSpK1iwoNm9e3eS7T/++KPjD+IefvjhZAu1xo0bmxo1atiYLGX3wsHzww8/bMaOHWuM8fxb3bx5s+NL2JCQEPfr062ioqJMSEiIDYkyZt68eWb27NnGGGO2bt1qgoKCjI+Pj/H39zcff/yxveEy4NSpU2b06NHmwQcfdHwJ+9JLL5nSpUu7P3CbNWuWGTZsmLn//vvNBx98YHe8NMXFxZlevXoZPz8/8/rrr5vHHnvMBAcHmyVLltgdLU0tW7Y0BQoUMK+99poZP368mTBhgsfFqYoXL27mzZuXZPvcuXMdWaZFRUWZP//80xhz84PAqKioFC9O5HK5ki2MEy+ZMmUyw4cPtztmmrJnz+5+Pf0ncPZ49nvU4cOHtWjRIo+lVzNlyqRevXpp7ty5NiZL2y+//KK2bdsm2f78889rzJgxNiRKn8Q5U1wulwYMGOAxR1B8fLw2b97s6KHj33//vd5///0k2wsVKqQzZ87YkCj9Dhw4oMceeyzJ9hw5cujixYvWB0qHCRMmyBijDh06aMiQIcqRI4f7Ol9fX4WEhDh+ifmEhIRkTy/86aefHLdUdXKOHTumSpUqJdnu5+enq1ev2pAo/W5dWjg8PFz79+/Xtm3bVLx4cccu0Z7o8uXLOnv2bJLtZ8+e1e+//25DovTbuXOntm7dqly5crm35cqVS2+99ZYeeughG5OlLGfOnHK5XHK5XCpZsmSS610uV5oLXdjtxx9/1Pz585Nsz5cvn86dO2dDovT75Zdfkp2cumbNmu6VV53s+eefd39dpUoVnThxwusW47h+/bq2bt2qzZs36/jx48qfP7/dkVL11VdfuecMjIyM1KOPPqrixYuraNGi+vDDDx2/kEXVqlV17do1rV69Wg8//LCMMRo9erSaN2+uDh066L333rM7YoqWLVumJUuW6JFHHrE7SoZ07txZPXr00PXr192TsUdHR+u1117Tq6++anO6pG5dnKJ9+/b2BblDq1atkjFGdevW1eLFiz2mxvH19VXRokVVsGBBGxOmzzPPPKNvvvlGL774ot1RLEEpZYPKlStr37597vk6Eu3bt08VKlSwKVX61KlTR2vXrlXx4sU9tq9bt06PPvqoTanStmPHDkk3JzL98ccf5evr677O19dXFSpUUO/eve2KlyY/Pz9dvnw5yfaDBw8qb968NiRKv+DgYB0+fFghISEe29etW+fYJXwTX5BDQ0NVs2bNJEuce4P69etrwoQJmj59uqSbb26vXLmiQYMG6cknn7Q5XdpCQ0O1c+dO9zLWiZYvX67SpUvblOrOFC1aNMn9cKpmzZopMjJSY8eO9Vj+uU+fPmrevLnN6VJXsmRJxcTEJJnD6Ndff03ymuUU98LBc86cOfXLL78oNDTUY/uOHTscP99h8eLFtXDhQr3++use2xcsWKASJUrYlOrOZc2aVZUrV7Y7RrqsWrVK8+fP1+LFi5WQkKDmzZvr//7v/xy/gtqFCxfcxy6BgYG6cOGCJKlWrVrq0qWLndHSpWrVqpo0aZJ79T2Xy6W+ffuqfv36jp9HLVeuXF4x9+7t+vTpo/Pnz+ull15yz0Hm7++vvn37qn///janS503zq1au3ZtSTc/3CxSpIijJ8FPTfHixTVgwABt2rRJ5cqVS/Je5JVXXrEp2d3BROc2WLBggV577TV169ZNDz/8sCRp06ZNmjJlikaNGuXxhstpn6pPmzZNAwcO1LPPPuuR/ZNPPtGQIUM8Dp4bN25sV8wURUZGauLEiY6fiPJ2nTp10vnz57Vw4ULlzp1bP/zwgzJlyqSmTZvqscce04QJE+yOmKKRI0fqgw8+0KxZs/TEE09o6dKlOnHihHr27KkBAwaoW7dudkf0kFz5lxInP45++uknRUREyBijQ4cOqWrVqjp06JCCgoL03XffJTnAcJqZM2dq8ODBGjt2rDp27KiZM2fqyJEjGjlypGbOnKmWLVvaHTFV0dHRio6OTnbFFCdPWn3t2jX17t1bs2bN0vXr1yVJmTNnVseOHTVmzJgky4g7ydKlS/Xaa69p8ODBHq9PQ4cO1ahRozwWVXDa3+6JEydUuHBh+fj42B0lw3r37q3Nmzfrk08+UcmSJbV9+3bFxMSobdu2atu2rQYNGmR3xBQtXrxYLVq0UHh4uHv0xfr16xUdHa2FCxeqWbNmNidMXXx8vKKiolJ8rnHqwgSFChXShQsX1KBBA7Vu3VpPP/20/Pz87I6VLuXLl9e7776r2rVrKzw8XBUrVtQ777yjSZMmafTo0frpp5/sjnjHYmNjHf3/4YMPPtAXX3yhOXPmJFkV2RtcuXJF+/btU5YsWVSiRAlH/64T+fj46MyZM0mOGU+fPq0HHnhAf/zxh03JkvfDDz+obNmy8vHx0Q8//JDqvk57j3272z/ouZXL5dLRo0ctTHP3UUrZIK2DTpfLJWOMXC6X4xro9B4wOzG7N7t06ZL+/e9/a+vWrfr9999VsGBBnTlzRjVq1NDSpUsd/UbRGKMRI0Zo5MiR7pWA/Pz81Lt3bw0bNszmdEn5+Pik+amKU/8+b3fjxg19/PHH+uGHH3TlyhVVrlxZrVu3VpYsWeyOli4ffvihBg8erCNHjkiSChYsqCFDhqhjx442J0vdkCFDNHToUFWtWlUFChRI8njyhiW3r1696v69P/DAA45+jkl06+tT4u888RDn1u+d/Ld77do1nTx5MsmKXk4+eI6Li9PLL7+sqKgoxcfHK3PmzIqPj1erVq0UFRXlMVWBE23btk3jx4/3WFH41VdfTfb0Yafp2rWroqKi1KhRo2Sfa8aPH29TstTNmDFDzzzzjHLmzGl3lAwbP368MmXKpFdeeUXffvutnn76aRljdP36dY0bN07du3e3O2Ka5s2bp2nTpunYsWPulW0nTJig0NBQR69sW6lSJR05ckTGGIWEhCQZOXLPrUZmo0mTJkm6OR3BsGHDFBAQ4L4uPj5e3333nY4fP+4+E8Upbi3REo/nk6s6nHwc8E9FKWWDEydOpHtfbznlw5ts3bpVCxcuTPbA/9NPP7UpVfqsW7fOo2AIDw+3O1K6xcXF6fDhw7py5YrCwsI8XuCcZM2aNeneN3GIMO6ua9eu6cqVK44f3ZWoQIECGj16tONPhbjXrF69Ot3D9J32t3v27FlFRkZq2bJlyV7vDQfPJ0+e1O7du3XlyhVVqlTJK09/8zZBQUGaO3euV5ySfa86ceKE18wZKElTp07VwIED1aNHD7311lvavXu3ihUrpqioKM2ZM0erVq2yO2KK0ppfz0mjMps3b66oqCgFBgameeq7E997JI7SOXHihO6//36PDxcS51YdOnSoqlevblfEZJ04ccJ9yl5a77fvlffYgYGB2rlzp2OnREkv5pSyQVBQkFd86pyco0ePevWD/uOPP1bbtm0VERGhb775RvXr19fBgwcVExPj+GH60s05C249BcUbzJ49Wy1btlSWLFkUFhZmd5w0Oe3NakZ8+eWX6d7XiafX3uqPP/6QMUZZs2ZV1qxZdfbsWU2YMEFhYWGqX7++3fFSFRcXl+zkyd7svffe07lz5zRw4EC7o6SoTp06dke4Yz169NDFixe1efNm1alTR5999pliYmI0fPhwjR071u546VKkSBEVKVLE7hhpunz5svv0zbRO13baaZ638/X1dex8abfz9jfp0s1J2Rs0aKBp06a5S1dvmjNQkt59913NmDFDTZs21ahRo9zbq1at6ui5VSVnlU5pyZEjh/tDklsXy/EWx44dkyQ9/vjj+vTTTz0WEHGyxL/F69eva8iQIRowYECqp8HdC+6V8UWMlLJBQECAnn32WXXo0MHrCgYfHx/Vrl1bHTt21L///W/5+/vbHSlDypcvr//85z96+eWXlT17du3atUuhoaH6z3/+owIFCjhqlaPEobPp4eTJ7vLnz68//vhDzzzzjDp27Oh1b9bXrl2r999/X0ePHtUnn3yiQoUKad68eQoNDXXc3++9dHpt/fr11bx5c7344ou6ePGiSpUqJV9fX507d07jxo1z9ISyffv2VUBAgAYMGGB3lL9NvXr1dOzYMUfPYRAaGqrIyEi1b9/eK8qRWxUoUEBffPGFqlWrpsDAQG3dulUlS5bUl19+qdGjR2vdunV2R/SQuKJteowbN+4uJsm4WyfuTel0baef5plo7NixOnr0qCZPnuz4yXwjIyM1adIkZc+eXe3bt0817+zZsy1MljF58+bVhg0bvHYkYJYsWbR//34VLVrUfRxcrFgxHTp0SOXLl3fcHEHJiYuLS3YONW973sfdlSNHDu3cufOeL6Vu/Tv2ZoyUssEHH3ygqKgo1a1bVyEhIerQoYPatm3r+BV2pJvna8+ePVu9evVS165d1aJFC3Xs2NG9SpPTHTlyRI0aNZJ08xPGq1evyuVyqWfPnqpbt66jSqn0zgXhcrkcXUr9/PPP+uqrrxQVFaU6deqoWLFiioyMVLt27RQcHGx3vFQtXrxYbdq0UevWrbV9+3bFxsZKujnH14gRI7R06VKbE3q6/QDNm23fvt39N7Bo0SIFBwdrx44dWrx4sQYOHOjoUurPP//U9OnT9e2336p8+fJJ5r1w2pv09IiOjrY7Qpq6d++uqKgoDR06VI8//rg6duyoZs2aecVkslevXnWfnporVy6dPXtWJUuWVLly5Rw5T0p65xFxYlGycuVK9wpeTj5VKT3WrVunVatWadmyZSpTpkyS5xonjTi6tWiKioqyL8hf9Pzzz+u///2vxygjb+LNK9sePHhQHTt21IYNGzy2e0uJfC/54osvdOnSJbVt29buKClq2rSpPv/8c/Xs2dPuKEgHRkrZ6OzZs5o3b56ioqK0b98+RUREqEOHDmrcuLEyZ3Z2X3jjxg19+eWXioqK0vLly1WyZEl16NBBbdq0Ud68ee2Ol6L7779fy5YtU7ly5VS+fHn1799fzz33nDZu3KgGDRro0qVLdke8p8XExOiDDz7QnDlztH//fjVo0EAdO3bU008/7chVpypVqqSePXuqbdu2Hp9E7NixQw0bNtSZM2fsjnjPypo1q/bv368iRYro2WefVZkyZTRo0CCdOnVKpUqVck+a70SPP/54ite5XC7Hroh1r9i+fbuioqL00UcfuSfc7tChgypXrmx3tBQ99NBDGj58uCIiItS4cWPlzJlTI0eO1KRJk7Ro0SL3pPP4e508eVKFCxdOUp4ZY3Tq1CnHj7yIjIxM9XqnjjiqW7euPv300yQTnV++fFlNmzZ19HNkt27dNHfuXJUoUUJVqlRJMh2H0z908OaVbR955BFlzpxZ/fr1S3Zi/woVKtiULKlKlSqlu5R34gcPaXnwwQd16NAhRxeBiae/16tXL9m/VSd/oJ8R98pIKUoph3j33XfVp08fxcXFKSgoSC+++KL69evn+CVPY2Nj9d5776l///6Ki4uTr6+vnn32Wb399tsqUKCA3fGSaNWqlapWrapevXpp2LBhevfdd9WkSROtWLFClStXdtSninfCGya727x5s2bNmqU5c+aoQIEC+u2335QrVy7Nnj3bcXPCZM2aVXv37lVISIjHk/7Ro0cVFhamP//80+6IGbZ161Zdu3ZNjz32mN1RUlW+fHl16tRJzZo1U9myZbV8+XLVqFFD27ZtU6NGjSgE75KRI0cqf/786tChg8f2WbNm6ezZs+rbt69NyTLu+vXreu+999S3b19dv35d5cqV0yuvvKLIyEjHjeD54IMPdOPGDbVv317btm1TgwYNdOHCBfn6+ioqKkotWrSwO+I96dZT+W51/vx55cuXz9FvuLxZSsvM//rrrypUqJCuX79uU7K03QsfOnjryrbZsmXTtm3b9OCDD9odJU0ZOfPCm+bK8iapnbbncrkcPR1BRnjDe790MbDNmTNnzNtvv21Kly5tsmbNalq3bm1Wrlxp5s6da8qUKWOeeOIJuyOm6PvvvzddunQxuXLlMvfff7954403zNGjR813331n6tWrZx566CG7Iybr/Pnz5ueffzbGGBMfH29Gjhxpnn76adOrVy9z4cIFm9P9dQEBAebIkSN2x0jizJkzZsyYMSYsLMz4+/ubli1bmhUrVhhjjLly5Yp57bXXTJEiRWxOmVRoaKg7562/2zlz5pjSpUvbGe2OPfjgg8bHx8fuGGn65JNPzH333Wd8fHw8ngtHjBhhGjRoYGOye1vRokXN+vXrk2zftGmTCQkJsSFRxsXFxZkFCxaYBg0amEyZMplHHnnEzJo1ywwdOtTkz5/fPPfcc3ZHTNPVq1fNtm3bzNmzZ+2OcsemTJlihgwZYneMVLlcLvPrr78m2X78+HGTNWtWGxLd23bt2mV27dplXC6XWbVqlfv7Xbt2me3bt5sRI0aYokWL2h0ziV27dpn4+Hi7Y/ztrl69amJiYuyOkW5Vq1Y1a9eutTsG4ChOfe+XUYyUssGnn36q2bNn6+uvv1ZYWJg6deqk559/3mMY85EjR1S6dGnFxcXZFzQZ48aN0+zZs3XgwAE9+eST6tSpk5588kmPU69++uknhYSE6MaNGzYmTerGjRuaP3++IiIilD9/frvj3BVOHML59NNP6+uvv1bJkiXVqVMntW3b1j2fR6Jff/1VwcHBjpsTaeTIkfrggw80a9YsPfHEE1q6dKlOnDihnj17asCAAerWrZvdETPs9OnTun79ulesFnTmzBn98ssvqlChgvs5ZsuWLQoMDHT0J6XNmjVLdiSOy+WSv7+/ihcvrlatWqlUqVI2pEudv7+/9u3bl+QTRm8YHZg45+FHH30kHx8ftW3bVp06dfJ4rOzevVsPPfSQoybzvX79uh588EH93//9n+PndMkIJ0+OnzhR+8SJE9W5c2ePUenx8fHavHmzMmXKpPXr19sVMV1SOkXo1uea9u3bpzq6x0q3Tiyf3NuPLFmy6N13300yUtNut46oK1asmL7//nvlyZPH7lj/OCtXrtSbb76pESNGqFy5cknmUHP6apnbtm3Tvn37JEllypRRpUqVbE6UtuXLlysgIMC9sM+UKVM0Y8YMhYWFacqUKV6zKl/i843TRkmnV2r5161bp4ceesgr5s9Mla2V2D9UYGCgeeGFF8yWLVtS3OfatWtm8ODBFqZKn+LFi5sRI0aY06dPp7hPbGysiYqKsjBV+mXJksUcP37c7hh3jRPb8g4dOpgNGzakuk9CQoIj/78kJCSY4cOHm2zZshmXy2VcLpfx9/c3b775pt3R4GDt2rUzOXLkMEWLFjXNmzc3zZs3NyEhISZnzpzm2WefNaVKlTJ+fn5m3bp1dkdNonjx4mbevHlJts+dO9eEhobakCj9fHx8TEREhFm4cKGJi4tLdp8rV66Y9u3bW5wsbQULFjR79+61O8Y/Rp06dUydOnWMy+UyNWvWdH9fp04dU79+ffPCCy+YgwcP2h0zTf369TM5cuQwtWrVMr169TK9evUyjz76qMmRI4fp3r27eeKJJ4yPj4/5/PPP7Y5qjLk5Au3YsWPG5XKZ77//3hw/ftx9OX36tLlx44bdEZOVO3dus2nTJmNMyqPrvF3//v1NZGSk3TFSlXgc5uPj43FJ3OZUMTEx5vHHHzcul8vkypXL5MqVy7hcLlO3bl3HP5bKli1rlixZYowx5ocffjB+fn6mf//+5uGHH3bka+nt5syZY8qWLWv8/PyMn5+fKVeunJk7d67dsdJt5syZpkyZMsbX19f4+vqaMmXKmBkzZtgd665gpJQNrl275vi5olJy/PhxFSlSJMmk1MZLJgWtU6eOevbsqSZNmtgd5a5w4kipe0FcXJwOHz6sK1euKCwsTAEBAXZHSpcjR45o9uzZOnLkiCZOnKh8+fJp2bJlKlKkiMqUKWN3vDvy3nvv6dy5cxo4cKDdUVLUr18/Xb58WZMnT3Y/VyYkJKh79+7Knj273nrrLb344ovas2eP1q1bZ3NaT6NHj9bo0aM1ZswY1a1bV9LNlfdee+01vfrqq+rfv7/NCVN24sQJrxgBmJwRI0bo4MGDmjlzpuMXOrmXREZGauLEiY4fYZGSzp07q0iRIhowYIDH9uHDh+vEiROaMWOGBg0apCVLlmjr1q02pfR+L7zwgubOnasCBQro5MmTuv/++5UpU6Zk93XiyMD0aNu2rU6dOuXoFSnXrFmT6vW1a9e2KEnGtGjRQkePHtXcuXPdo2H37t2rdu3aqXjx4vroo49sTpiygIAA7d69WyEhIRo8eLB2796tRYsWafv27XryyScdPb/nuHHjNGDAAHXt2lWPPPKIpJujiqZMmaLhw4c7flW+gQMHaty4cerWrZtq1KghSdq4caMmT56snj17aujQoTYn/HtRStnAmyfW9ObskrRw4UL1799fPXv2THYlhvLly9uU7O/h1Mnurl69qjVr1ujkyZNJTkm9V1a/cJo1a9aoYcOGeuSRR/Tdd99p3759KlasmEaNGqWtW7dq0aJFdke8I04+JShR3rx5tX79epUsWdJj+8GDB1WzZk2dO3dOP/74ox599FFdvHjRnpApMMaoX79+mjRpkvtv1d/fX3379nV0ESgpxdNqLl68qMqVKzv6MdOsWTNFR0crICBA5cqVS/La5A2LcOzduzfZ5/jGjRvblOjelyNHDm3btk3Fixf32H748GFVqVJFly5d0v79+/XQQw/p999/tyllyrzpMbN8+XIdPnxYr7zyioYOHars2bMnu1/37t0tTgany5Ejh7799ls99NBDHtu3bNmi+vXrO+444Fa5c+fWunXrFBYWplq1aqlt27Z64YUXdPz4cYWFhTl6JeTQ0FANGTJEbdu29dg+Z84cDR48WMeOHbMpWfrkzZtXkyZN0nPPPeex/aOPPlK3bt107tw5m5LdHXwcZ4OUesDY2Fj5+vpanCZjUsp+5coV+fv7W5wm4xKXur21CHG5XDLGyOVyOb5US4sTO+YdO3boySef1LVr13T16lXlzp1b586dU9asWZUvXz7HlVLNmzdP975OfqPYr18/DR8+XL169fI4eK5bt64mT55sY7K/Jjo62u4Iabpx44b279+fpJTav3+/+znG39/fMXMb/PDDDypbtqx7vpe3335bAwYM0L59+5QlSxaVKFHCK+YqOH78eLLP4bGxsfr5559tSJR+OXPm1L/+9S+7Y9yRo0ePqlmzZvrxxx/dr6fS/+a+cPrr6tatW7Vw4cJkyxEnP8dLN59HNmzYkKSU2rBhg/uYLCEhwXHHZ974mGnQoIGkm/MCJY569UZz585VixYtkjynx8XF6eOPP07yBt5p1q5dq/fff19Hjx7VJ598okKFCmnevHkKDQ11z3vkNAkJCUnmv5Kk++67z3Fzqd6uVq1a6tWrlx555BFt2bJFCxYskHTzQ7b777/f5nSp++WXX1SzZs0k22vWrKlffvnFhkQZc/36dVWtWjXJ9ipVqjhu3ua/A6WUhSZNmiTp5ovuzJkzPU4Bio+P13fffefYyXsTJwV1uVwaOHBgspOCVqxY0aZ06ef0Vjw9bj94u9WyZctUqFAhqyOlqmfPnnr66ac1bdo05ciRQ5s2bdJ9992n559/3pGfKObIkcP9tTFGn332mXLkyOF+Ydi2bZsuXryYofLKDj/++KPmz5+fZHu+fPnuuU9XnKZNmzbq2LGjXn/9dfcno99//71GjBjhPuBfs2aNY06hrFSpUrKT+N7+qa5Tffnll+6vv/76a4+/4fj4eEVHRyskJMSGZOk3e/ZsuyPcse7duys0NFTR0dEKDQ3Vli1bdP78eb366qt655137I6XqsQ34REREfrmm29Uv359HTx4UDExMWrWrJnd8dLUrVs3vfjii9q2bZvHc83MmTP1+uuvS7r5N+G04zNvfsx489+qdPOU1QYNGiQ54+H3339XZGSko0upxYsXq02bNmrdurW2b9+u2NhYSdKlS5c0YsQILV261OaEyatbt666d++ujz76SAULFpQk/fzzz+rZs6fq1atnc7rUTZ48WS+99JIWLVqkqVOnut9jLFu2zF3UOlXx4sW1cOFC93NhogULFqhEiRI2pUq/Nm3aaOrUqRo3bpzH9unTp6t169Y2pbp7OH3PQomrGZ04cSLJ+ei+vr4KCQnR0KFDVb16dbsipihx5ZY1a9aoRo0aHiO6ErP37t3bK/7IvdV///tfjR8/XocOHZIklShRQj169FCnTp1sTpa6nDlzavPmzSpVqpRy5sypjRs3qnTp0tq8ebPatWun/fv32x0xRX379tWFCxc0bdo0999rfHy8XnrpJQUGBmrMmDE2J0zZ/fffr4ULF6pmzZoec4199tln6t27t44cOWJ3xDQdOnRIq1at0q+//prk00Qnn0oWHx+vUaNGafLkyYqJiZEk5c+fX926dVPfvn2VKVMmnTx5Uj4+Po74pDFPnjxaunSpqlevLh8fH8XExChv3rx2x0q3xHm7bh1xkei+++5TSEiIxo4dq6eeesqOePe8oKAgrVy5UuXLl1eOHDm0ZcsWlSpVSitXrtSrr76qHTt22B0xReXLl9d//vMfvfzyy+7nydDQUP3nP/9RgQIFNGTIELsjpunDDz/U5MmTdeDAAUlSqVKl1K1bN7Vq1UqS9Mcff7hX43MKb37MpMQb5juUlOJz/K5du/T444/rwoULNiVLW6VKldSzZ0+1bdvW47hmx44datiwoWPnNzp16pQaN26sPXv2qHDhwu5tZcuW1ZdffumI44B70eLFi9WiRQuFh4e755Rav369oqOjtXDhQsd/8NCtWzfNnTtXhQsX1sMPPyxJ2rx5s06ePKm2bdt6jL67vbjyRpRSNnj88cf16aefes0ymrfy9klBE3nTPAaSd092lzdvXm3YsEElSpRQyZIl9e677yoiIkL79+9XlSpVdPXqVbsjpihv3rxat26dSpUq5bH9wIEDqlmzps6fP29TsrT17t1bmzdv1ieffKKSJUtq+/btiomJUdu2bdW2bVsNGjTI7oipmjFjhrp06aKgoCAFBwd7jAx0uVzavn27jenS7/Lly5KcvVT1vTKJb2hoqL7//nsFBQXZHeVv8/rrr+vMmTOaNWuW3VFSlCtXLm3fvl2hoaF64IEHNHPmTD3++OM6cuSIypUr5+g5R7Jly6Y9e/YoJCREefLk0erVq1WuXDnt27dPdevW9YpTPLyRNz9mUuL0+Q4rVaokl8ulXbt2qUyZMh4LKsTHx+vYsWNq0KCBFi5caGPK1GXNmlV79+5VSEiIRyl19OhRhYWF6c8//7Q7YoqMMfr222/dH8SWLl1a4eHhNqdKn/j4eH3++efat2+fJKlMmTJq3LhxiscJTrJt2zaNHz/enb106dJ69dVXValSJZuTpS1xQEhaXC6XVq5ceZfT3H2cvmeDW1e2SO1ULCfy9mHL3jiPgSRNnTpVM2bM8JjsrnHjxipfvry6devm6FKqUqVK+v7771WiRAnVrl1bAwcO1Llz5zRv3jyVLVvW7nipSpwb6PZSav/+/Y6fB2DEiBF6+eWXVbhwYcXHxyssLEzx8fFq1aqV3nzzTbvjpWn48OF666231LdvX7uj/CVOLqMSTZ8+Xc2bN3dP4tu5c2evnC/lXjg9+3Y//fSTTp06ZXeMVJUtW9Y9wqh69eoaPXq0fH19NX36dMctunG7XLlyuScAL1SokHbv3q1y5crp4sWLXlmMeAtvfsykxOnzHTZt2lSStHPnTkVERHhMIZJ4xoPT57ULDg7W4cOHk5yOvW7dOsc/blwul5544gk98cQTdkfJkMOHD+vJJ5/Uzz//7D4WHjlypAoXLqwlS5bogQcesDlh6qpUqaIPPvjA7hh3xMkrYd4VBraYOXOmKVOmjPH19TW+vr6mTJkyZsaMGXbHumNTpkwxQ4YMsTtGmp566inTpEkTc/bsWRMQEGD27t1r1q5da6pVq2a+++47u+OlKEeOHObgwYNJth84cMDkyJHD+kAZ8P3335uVK1caY4yJiYkxERERJnv27KZy5cpm586dNqdLXc+ePU2ePHnM2LFjzdq1a83atWvNO++8Y4KCgkzPnj3tjpcuJ06cMEuWLDELFixI9jHkVNmzZzdHjhyxO0a6VapUyVy4cMEYY0zFihVNpUqVUrw4Wfv27c3ly5ftjnHHvv32W9OoUSNTrFgxU6xYMdOoUSOzYsUKu2Pd05YvX24WL15sjDHm0KFDplSpUsblcpmgoCDz7bff2pwudc8995wZO3asMcaYoUOHmrx585pOnTqZokWLmmbNmtmcLnm5cuUyZ8+eNcYYkzNnTpMrV64UL06V2mMmOjra5nT3tqioKPPHH3/YHeOOjBgxwoSFhZlNmzaZ7Nmzm7Vr15oPPvjA5M2b10yaNMnueKny1temhg0bmgYNGpjz58+7t507d840aNDAPPnkkzYmS96lS5c8vk7tAmfh9D0bePOpWClx+rDlRN46j0G3bt103333JTlnuHfv3vrjjz80ZcoUm5Ld2xISEvTOO+9o4sSJ7tM4ChQooO7du+vVV1/1iqHL3qpjx4566KGH9OKLL9odJV2GDBmiPn36KGvWrGnOQ+P0Uye91Xvvvafu3bvr3//+t/u1ddOmTVq0aJHGjx+vl19+2eaEKfP2FbFud+HCBeXKlcvxo8AvXLigP//8UwULFlRCQoJGjx7tPt38zTffdOQ0C3PmzFHLli3l5+enqKioVH/H7dq1szDZX+MtjxnJe+c7vFVcXFyy+YsUKWJTorQZYzRixAiNHDnSPZLRz89PvXv31rBhw2xOlzJvfm3Kli2bNm3apHLlynls37Vrlx555BFduXLFpmTJy5Qpk3vhlsQVhW9nvGjFdW9eHTajKKVskDdvXk2aNMnjVCxJ+uijj9StWzdWxrqLvHUeg3/aZHdOlNrcQOvXr1fVqlWTvKG0kzFGixYtSvHA2ekvZiNHjtS4cePUqFEjlStXLslyyq+88opNyf6ZvGES3/vvv1/9+vVT165dPbZPmTJFI0aM0M8//2xTsrTdeiB9q/PnzytfvnyOPnju0KGDJk6cmOSUz6tXr6pbt26OnQ/rxo0bmj9/viIiIpQ/f36748BLePt8h4cOHVKHDh20YcMGj+3e9EY9Li5Ohw8f1pUrVxQWFuZxKqITefNrU+7cufV///d/qlmzpsf29evX6+mnn3bcxPhr1qzRI488osyZM2vNmjWp7lu7dm2LUt2ZtFaH9fYpdW5HKWWDnDlzuufYudXBgwdVrVo1Xbx40Z5g/wCPPvqoXn31VTVt2lStWrXSb7/9pjfffFPTp0/Xtm3btHv3brsjJsvbJrtLnFAzPZx+AJcegYGB2rlzp6PmNOjevbvef/99Pf7448qfP3+S/x9OfzFLXK00OS6Xy9GjMk+dOiWXy+VeUWfLli2aP3++wsLC9MILL9ic7s54w2jYgIAA7dy5U8WLF/fYfujQIVWqVMlxn+jeyptXxEqpUDt37pyCg4N148YNm5KlLWvWrNq3b5+KFi1qd5Q7sn37dt13333uUQxffPGFZs+erbCwMA0ePNhjpWS7NW/ePN37OvlDk6JFi+qll17y2vkOE9+w9+vXTwUKFEhybFChQgWbkqXNWwtwb35tatu2rbZv367//ve/qlatmqSbH4p37txZVapUUVRUlL0BU3Djxg2NGDFCHTp08NrVDe+F1WEzgonObdCmTRtNnTo1yYiW6dOnq3Xr1jalSp+RI0cqf/786tChg8f2WbNm6ezZs45/kX7zzTfdq70NHTpUTz31lB599FHlyZNHH3/8sc3pUuZtk90lTqj5T+HEbn/evHn69NNP9eSTT9od5Y5486TVrVq10gsvvKA2bdrozJkzCg8PV9myZfXhhx/qzJkzjh5tlBKnT+Ir3Vz84bPPPlOfPn08tn/xxRd66qmnbEqVusQC3+VyqV69eimuiOVEly9fljFGxhj9/vvv8vf3d18XHx+vpUuXJimqnKZatWrauXOn15ZS//nPf9SvXz+VK1dOR48eVYsWLdS8eXN98sknunbtmiZMmGB3RLccOXLYHeFv8dtvv+mZZ56xO8Yd27lzp7Zt26YHH3zQ7igZNmfOHI0aNSpJKfXHH39o7ty5ji2lvPG1KdGkSZPUrl071ahRwz1i/caNG2rcuLEmTpxoc7qUZc6cWWPGjPG6U99vdeTIETVq1EjSzcUIrl69KpfLpZ49e6pu3bqUUrgzvXr1cn/tcrk0c+ZMffPNN8meiuVk77//vubPn59ke5kyZdSyZUvHl1IRERHur4sXL679+/d71TwG3oI5c+yXI0cOR43c+ifZvXu3+xPFhQsXqly5clq/fr2++eYbvfjii15ZSjnVpEmT3F+HhYXprbfe0urVqz3m7Vi/fr1effVVuyKmyptXxMqZM6e7UCtZsmSS610ul+MPml966SX16tVLp06dUpUqVZQtWzaP68uXL29TsvQ5ePCgKlasKEn65JNPVLt2bc2fP1/r169Xy5YtHVVKOX10bno988wz7udybxQWFuZ104R4ewGe1mvTra9jTpuaIGfOnPriiy906NAh7d+/X5JUunTpJKO+nKhu3bpas2ZNktUavcU/bXVYTt+ziLedfpUSf39/7du3L8mpNUePHlVYWJj+/PNPm5Klj7cO/ZW8f7K7rVu3at++fZJuvkBXqVLF5kR/n8RhtU4qgebMmaPly5dr1qxZypIli91x7shPP/2kL7/8MtnHvJPnTgsICNDu3bsVEhKixo0b65FHHlHfvn118uRJlSpVSn/88YfdEVPlTZP4pnaa562cfsrnnDlz1KJFC483W063Zs0aGWNUt25dLV68WLlz53Zf5+vrq6JFi6pgwYI2Jkybj49Pkm0ul8tr5tcJDAzUtm3bVKJECT3xxBN66qmn1L17d694rrlx44ZWr16tI0eOqFWrVsqePbtOnz6twMBAR88R5O3zHa5cuVJvvvmmRowYkWz+5ObNtFtKE1YnSizA33jjDQtTpd+98jrlbaZNm6YhQ4aodevWyX7o0LhxY5uSpU+rVq1UtWpV9erVS8OGDdO7776rJk2aaMWKFapcubJXvPfLCEopZEiJEiU0aNAgPf/88x7b582bp0GDBjn+ydRb577w5snufvrpJz333HNav369cubMKUm6ePGiatasqY8//thrz/W+lRNLqT/++EPNmjXT+vXrFRISkuTA0+lzeUVHR6tx48YqVqyY9u/fr7Jly+r48eMyxqhy5cqOLu+rV6+uxx9/XI0aNVL9+vW1adMmVahQQZs2bdK///1v/fTTT3ZHTJG3T+Lr7bxxRawTJ06ocOHCyRY8TnfixIlUr3f6aX1169ZV4cKFFR4ero4dO2rv3r0qXry41qxZo3bt2un48eN2R0zWiRMn1KBBA508eVKxsbE6ePCgihUrpu7duys2NlbTpk2zO2KKvHm+Q+l/ReztJY+Ti9h7oQD3Vree6ZMWp31YmNprklMf67fyxtVh/wpO30OGdO7cWT169ND169dVt25dSTffPL722muOPT1C8v6hvyNGjHAvG5s9e3ZNnDjRY7I7J+vUqZOuX7+uffv2qVSpUpKkAwcOKDIyUp06ddLy5cttTvjXOfHUz3bt2mnbtm16/vnnk53o3On69++v3r17a8iQIcqePbsWL16sfPnyqXXr1o6dYyfR22+/rWbNmmnMmDFq166de+LYL7/80n1an1MNHz5cb731luNPxb7XePOKWInFzbVr15Id1ejkU+CcXjqlZfz48Xr++ef1+eef64033nCfUrNo0aIkq2U5Sffu3VW1alXt2rVLefLkcW9v1qyZOnfubGOytHnzfIeS981RKv1vlbRjx455ZQG+e/dulS1bNtnrPv/8c0fPw7pjxw7t2LFD169fdx/DHzx4UJkyZVLlypXd+znxGPP2D3e8za3lq4+Pj/r162djmruPkVI2ePzxx1P943XyCABjjPr166dJkya5Dzz9/f3Vt29fx53WcStvH/qbLVs27dmzRyEhIcqTJ49Wr16tcuXKad++fapbt65++eUXuyOmKEuWLNqwYYMqVarksX3btm169NFH74nzop04Uipbtmz6+uuvVatWLbuj3JHs2bNr586deuCBB5QrVy6tW7dOZcqU0a5du9SkSRPHjgBIFB8fr8uXL3t8knX8+HFly5YtyQprTuLElSTT6/YFOG7n5NOzvXlFrLNnzyoyMlLLli1L9nonF2pz585N9Xqnz/OZkj///FOZM2f2mDjfSfLkyaMNGzaoVKlSHq+fx48fV1hY2D1xXIC74+LFi9qyZUuyI0qd+vdaqFAhrVu3Lskou8WLF6tt27buBZicaNy4cVq9erXmzJnjPp757bffFBkZ6V7RHHdHSmf3nD9/Xvny5XP0a+udcOar1T0ucVLKRNevX9fOnTu1e/dutWvXzp5Qqfjhhx9UtmxZd7Hz9ttva8CAAdq3b5+yZMmiEiVKyM/Pz+6YqVq1apVXD/315snuChcurOvXryfZHh8f7+jfeaL0zHuR+P/GSQoXLuzIuSHSK1u2bO7iu0CBAjpy5IjKlCkjSY6fpLVu3br69NNPkwytzp07t5o2beroDx68eRLf3377zeP769eva/fu3bp48aJ7ZK9TefOKWD169NDFixe1efNm1alTR5999pliYmI0fPhwjR071u54qerevbvH99evX9e1a9fk6+urrFmzOvZNbqJixYrp+++/9xhtJN0spSpXruzYU8kSEhKSfUP1008/JZnz04m8db7DRGvXrtX777+vo0eP6pNPPlGhQoU0b948hYaGOvqDrK+++kqtW7fWlStXFBgYmOT0cqf+vXbq1Enh4eFav369goODJUkLFixQhw4dFBUVZW+4NIwdO1bffPONx/FMrly5NHz4cNWvX9/xpVR0dLTGjx/vntO2dOnS6tGjh8LDw21OlraUxg3FxsbK19fX4jR3H6WUDcaPH5/s9sGDB+vKlSsWp0lbpUqV3E3trQdADz30kN3R0s3bh/4+9thjWrFihcqVK6dnnnlG3bt318qVK7VixQrVq1fP7nipGjNmjLp166YpU6aoatWqkm5Oet69e3e98847NqdL3e3zXjzxxBPKnj273n77bcfPezF27Fi99tprmjZtmleuPPLwww9r3bp1Kl26tJ588km9+uqr+vHHH/Xpp5+6Vy11qtWrVyd5oyLdfKO4du1aGxKlX/HixTVgwABt2rTJ6ybx/eyzz5JsS0hIUJcuXfTAAw/YkCj9vHFFrEQrV67UF198oapVq8rHx0dFixbVE088ocDAQI0cOdK9pLUT3V5kSjdPpezSpUuS5dud6Pjx48mWO7GxsY6eu65+/fqaMGGCpk+fLulmoXDlyhUNGjRITz75pM3pUpfWfIdOt3jxYrVp00atW7fW9u3bFRsbK0m6dOmSRowYoaVLl9qcMGWvvvqqOnTooBEjRihr1qx2x0m3IUOG6MKFCwoPD9d3332n5cuXq1OnTpo37/+1d+dxNeZ//8BfpxTSdqTGWjkKRdEyMWNSMQgj5GvGiKyNtZpIZb53UYxtpjLZwhTKNpZmMExMK1lCy4lbtEjZd9NUTIvz+6Nf53acSkz1ua7T+/l4fB93c13nj9fDfbq6rvf1+bzf0ZydrlqjuLgYjx8/ljv++PFjTr6QfdPmzZvh6emJ//znP9IXEBcuXMCoUaOkLVG4qGYao0AgwM8//ywz+KGqqgqnT5/m5Qusd6HtexySl5cHGxsbPHv2jHUUGTo6Ojhx4gQGDBgAJSUlPHz4kNPbTxqCb70v+NzsTigUoqysDJWVldKtBDU/vz0Jg2vf/XHjxkFDQwMRERHQ0dGRbjFISkqCm5sbcnNzWUes05v/7mpqanLFBa79W7/t5s2bKCkpgbm5OUpLS7F48WLpdz4kJISTvWCysrIAVK+GTUhIkFmRWVVVhdjYWGzdupXTWw/53sS3Njdu3IC9vT2ntznzcSJWDU1NTWRlZcHQ0BAGBgbYu3cvBg0ahIKCAvTp04fzq3lrc/nyZUyZMkU6Ap1rjh49CqD6b9SuXbugpaUlPVdVVYX4+Hj8+eefuHHjBquI9bp9+zYcHR0hkUiQm5sLa2tr5ObmokOHDjh9+jSne3za2Nhg5MiR0n6HYrFYpt/hvHnzWEesl4WFBby8vODq6iqzdTIjIwMjR47EgwcPWEesU7t27XDlyhVebi8HABcXF1y6dAl3797F3r17MXbsWNaR3snV1RVnzpxBcHCwtCdmamoqlixZAltbW+zatYtxwrp17doVfn5+WLhwoczxTZs2YdWqVbh79y6jZPWruQ8rLCxE165doaysLD2nqqoKQ0NDBAUFYcCAAawiNglaKcUh58+f5+Q46AkTJsDOzk7a58La2lrmF+RNXH9g4WvvCz43u1u/fj3rCB/szJkzOHfunNwyWUNDQ87+MavB5393ADI3ne3ateP0qrQa/fv3h0AggEAgqHW7WNu2bbFhwwYGyRqO7018a5Ofn8/Zyao1arYSvL3ylQ+Nznv16oUbN27A0NAQ/fr1w9atW2FoaIjw8HDOD+KoS6tWrXDv3j3WMepU0xhZIBDItX1QUVGBoaEhp7dOduvWDWKxGL/88gvEYjFKSkowa9YsuLi4oG3btqzj1Ss7Oxv79u0DUP09efnyJdTV1REUFISxY8dyvih148YNDB48WO64lpYWXrx40fyB3sOIESNw+fJlXhSlagrHb3J2dsaZM2fw9ddfQyAQSD/j5OTU3PEaLDw8HN7e3pg8ebK0FUerVq0wa9Ys/PDDD4zT1e/Fixe1DsYZPnw4p4e51NyHOTg44Ndff5VOLld0VJRiwNnZWea/JRIJ7t+/j8uXL8Pf359Rqrpt27YNzs7OyMvLg4eHB9zc3Hix5782fO19ceLECSgrK2PEiBEyx0+dOoWqqiqMHDmSUbJ342KftIbic98LPv+781VBQQEkEglEIhEuXrwos6JUVVUVenp6dRb0yb/39ujqmr+tx48f5/zvAx8nYtXw9PSUrkJbtmwZHB0dsXv3bqiqqnL6LTog/+BY853ZuHEjBg0axCjVu9U0eO7evTsuXbqEDh06ME7UcBUVFejduzd+//13uLi4wMXFhXWk98LnfocA0LFjR+Tl5clt609JSeF8sWf06NFYsmQJrl27VuuKUi4Vd+qbqBcZGSkdvMH1lw5qamrYvHkzfvjhB+Tn5wMAevToIbfTgYucnJzw66+/ym3FPnLkCL744gtGqRqmoqICRUVFuH//fospStH2PQZmzJgh899KSkrQ1dXFkCFDMHz4cEapGmbGjBkICwvj/AN5XTp16oQjR47AxsYGmpqauHz5Mnr27ImjR49i3bp1SElJYR2xVubm5lizZo1cr4XY2Fj4+vpCLBYzSvZu6enpUFFRgZmZGYDqPwY7duyAqakpli9fzulmfV999RW0tLSwbds2aGhoICsrC7q6uhg7diz09fWxY8cO1hEb5NWrV3JbVbm4HUgoFDZ4rDDXtx/yGV+b+Do4OMj895t/W2fOnMnZSWSKpqysDNevX4e+vj7niyVv95cUCATS70xwcDBvV3pxXZcuXRAXFwcTExPWUd7buHHjMHr0aLi5ucHb2xtHjhzB9OnTpcMt4uLiWEes1+rVq7F7925ERkZi2LBhOHHiBAoLC+Hl5QV/f3+4u7uzjlin+vrBcr24Q5rfypUr8eOPP2LQoEH45JNPAFT3lDp79iwWL14scx/MxX6ZfL5OfggqSpEWha+9L9q2bYvs7Gy5N1u3bt1Cnz59OD1O9uOPP4afnx8mTJiAmzdvwtTUFM7Ozrh06RJGjx7N6W1md+7cwYgRI3jZ96K0tBS+vr44cOAAnj59Kneeizdv77OqgusrX3Jzc5GYmFjr2OqAgABGqd7tXU18uTw5kO/4NBHr7VVp9eFyIVMRxMfHIz4+vtZrTc1qDK5ZtWoVcnJy8PPPP/OuWMzHfodvkkgkWLVqFVavXi29523dujW8vb2xYsUKxukIX2zevBlPnjzh9P1MfT0y38TVfpl8vk5+CCpKMXD79m0IBAJ07doVAHDx4kXs3bsXpqam+Oabbxin+zB8uDgB1QWSlStXYsSIEXBycoK2tjZWr16NsLAwHDp0SLo0lWs6duyIvXv3yvWpiYuLw+TJk/Ho0SNGyd5NS0sL6enp6NGjB9auXYuEhAScPHkSZ8+exaRJk3D79m3WEetVWVmJ/fv3IysrCyUlJbC0tORF34sFCxYgMTERK1aswNSpU7Fp0ybcvXsXW7duxZo1azi/ZcLV1RX29vaws7Pj/OS0t23fvh3z5s1Dhw4d0LFjR7mx1enp6QzT1Y/PTXxfvnwJiUQincpUWFiIX3/9FaamppxfhfzmRKzo6Ghcu3YNIpEIGzduxIkTJzg3EevtVWnp6emorKxEr169AAA5OTlQVlaGlZUVFTKbUGBgIIKCgmBtbS3t+/mm2iZScsH48eMRHx8PdXV1mJmZyW0FiomJYZSs5SgvL0deXh5KSkpgamoqM+GLNC4PDw8YGRnJrcbZuHEj8vLyOP1yti5Dhw5FQUEBJ4s5iqKlXSepKMWAra0tvvnmG0ydOhUPHjxAz5490bdvX+Tm5sLd3Z3zhZ3a8OXitHv3blRWVmL69OlIS0uDo6Mjnj17BlVVVezcuRNfffUV64i1mjNnDs6fP49ff/1V+oCel5eHCRMm4OOPP8bPP//MOGHdNDU1kZaWBmNjYwwbNgxffPEFPD09UVRUhF69euHly5esIyokfX19REVFwd7eHpqamkhPT4eRkRGio6Oxb98+zj3kvs3NzQ3JycnIz89H586dYWdnJy1SGRsbs45XLwMDA8yfP5/TjTTroqGhgczMTPTo0QNCoRApKSno06cPxGIxxo4dy+nJgcOHD4ezszPmzp2LFy9eoFevXlBVVcWTJ08QEhLC6YIanydihYSEICkpCbt27ZJOgn3+/DlmzJgBW1tbLF68mHFCWYq0yqtTp05Yt24dpk6dyjrKe3m7jcXb+LI1XhEUFxcjISEBvXr14sU2odLSUiQnJ9e6vZyLW7CA6m1YR48ehZWVlczx9PR0ODk54c6dO4ySES5raddJKkoxIBQKceHCBfTq1QthYWH45ZdfcPbsWZw6dQpz587lfGFHkfCl98Vff/0FR0dHXL58WbrC7s6dO7C1tUVMTAynm+ANGTIE3bp1w+eff45Zs2bh2rVrMDIyQnJyMqZNm8bph1wAuHfvHlJSUmrdGsHVGyAAUFdXx7Vr16Cvr4+uXbsiJiYGNjY2KCgogJmZGUpKSlhHbJC7d+/i9OnTSE5ORnJyMnJyctCpUydO38RpamoiMzOT801ja9OxY0ckJibCxMQEpqamWLNmDZycnCAWizFo0CBOf286dOiA5ORk9OnTBz///DM2bNiAjIwMHD58GAEBAcjOzmYdsU5qamq4du0aDA0NZYpSNVueX716xTpinbp06YJTp05Jmz3XuHr1KoYPH865KXaKtMpLR0cHFy9e5N1q0oY6e/YsrK2t0bp1a6Y5FKnf4ZdffonBgwdj4cKFePnyJfr37y8d0rF//35MmDCBdcQ6ZWRkYNSoUSgrK0NpaSnat2+PJ0+eQE1NDXp6epx9fmrTpg2uXr0KIyMjmeN5eXno27cvp6/viujIkSP466+/4OrqyjoKeYPib1DkoIqKCukf2Li4OOm0iN69e0sn2JDmoaamBktLS9Yx3klLSwvnzp3Dn3/+CbFYjLZt28Lc3LzWsb5cs379eri4uOC3337Df//7X+kf5UOHDuHTTz9lnK5+O3fuxJw5c6CqqgodHR25bVhcLkqJRCIUFBRAX18fvXv3xoEDB2BjY4Njx45xuoj5NqFQCB0dHQiFQmhra6NVq1YyU+24aOLEidKXDHwzcOBApKSkwMTEBKNGjcLixYtx5coVxMTEYODAgazj1ausrEw6hOPUqVNwdnaGkpISBg4ciMLCQsbp6sfniVjFxcV4/Pix3PHHjx/j77//ZpCofm9OOgwJCYGGhkadq7y4bvbs2di7dy8nJzc3hpEjR3KiwM/H7VV1OX36NP773/8CqN7e+fr1a7x48QK7du3CypUrOV2U8vLywpgxYxAeHg4tLS1cuHABKioqmDJlCjw9PVnHq5ORkRFiY2OxcOFCmeN//PEH8+92Q/C1R2ZdfH19kZubS0UpjqGVUgwMGDAADg4OGD16NIYPH44LFy6gX79+uHDhAv7zn/9wegUAwL+LkyIt1Vckr169grKystxIXy7p1q0b5s6di6VLl9Y79YWLQkNDoaysDA8PD8TFxWHMmDGQSCSoqKhASEgIp2/gAOC7775DUlISMjIyYGJiIt2+N3jwYOnDI1etXr0aISEhGD16dK1jq7lczORzE19zc3PMnj0b48ePR9++fREbG4tPPvkEaWlpGD16NKe3wPF5IparqyvOnDmD4OBg2NjYAABSU1OxZMkS2NravtcAg+bGt1Veb/P09ERUVBTMzc1hbm4ud63h+z3Nm6sGuYLP/Q6B6sE5OTk56NatG1xdXdG5c2esWbMGRUVFMDU15fRqWG1tbaSmpqJXr17Q1tbG+fPnYWJigtTUVEybNg3Xr19nHbFWkZGRWLhwIZYsWSLtDRsfH4/g4GCsX78ebm5ujBPWjc89MhXVd999hwcPHnB2kMWHoqIUA0lJSRg/fjyKi4sxbdo06Zfqu+++w/Xr1znduIyPFydFWqr/tsuXL6OsrIwXK6b4SJG2RhQWFiItLQ1GRkYwNzdnHeedlJSUoKurCy8vLzg7O6Nnz56sIzVYfRNfuDrlRREcOnQIkydPRlVVFYYOHYpTp04BqC74nD59Gn/88QfjhHXj80SssrIyeHt7IzIyEhUVFQCAVq1aYdasWfjhhx/kmrNyiYaGBo4dOwZ7e3uZ44mJiXBycuLkSq83vX1/8yaBQMC7e5q3cbEoxed+hwDQs2dPrFy5EqNHj0b37t2xf/9+DBkyBGKxGEOHDsWTJ09YR6yTrq6u9CVJz549sWHDBowYMQLXr1+HlZUVpydRb9myBd9//7200G1oaIjly5dzfrUOn3tkKipXV1fcvn1bZtWvIqCiFCNVVVUoLi6WeeN/69Yt6b5ogDt76d/E94sT3xqyvouJiQlycnJQVVXFOoqM9u3bIycnBx06dHhnLwYu91/w8fFB+/bt4efnxzrKe6moqICjoyPCw8N5cZNcG7FYjOTkZCQlJeHMmTNQVVWV3vzb29vzqkhFms+DBw9w//599OvXT7q68eLFi9DU1ETv3r0Zp3s3Pk/EKi0tlU6w7dGjB6eLUTX4vMqrJeBiUaoGH/sdAtXTsj09PaGurg59fX1kZGRASUkJGzZsQExMDKcfdIcPH47p06dj8uTJcHNzQ1ZWFjw8PBAdHY3nz58jNTWVdcR3evz4Mdq2bcubazufe2TGxsZCXV0dn332GQBg06ZN2L59O0xNTbFp0ybOr7pvaagoxWFcvBBwMdP74PtS/bfdu3cPFRUVnNtSs2vXLkyaNAmtW7d+5039tGnTminV+6uqqsIXX3yBly9f1roNi8tbI958o6gIxGIxQkNDsWfPHrx+/ZpzhVg+U6QmvoqCbxOx+IrPq7xaAi4XpcrKypCSkoLExEQkJSUhPT0dpqamyMjIYB3tnS5fvozbt29j2LBh0uLI8ePHoa2tjUGDBjFOV7fLly/j77//hoODAx49egRXV1fpfU5kZCT69evHOqLCmTVrFj7++GNe9sg0MzPD2rVrMWrUKFy5cgUff/wxFi1ahMTERPTu3Zvz0+uioqLw1VdfyS1OKS8vx/79+zm/yu59UVGKw7j4x5jPFyeA/0v1SfNauXIlAgIC0KtXL3z00Udy21W5vDXCy8sLrVu3xpo1a1hH+SASiQQZGRlISkpCUlISUlJSUFxcDHNzc9jZ2SE0NJR1xHrduXMHR48erXVsNdeKme+zGoTLReS6bN68GU+ePOFkz8MafJ6IpQj4uMqrxuXLl3HgwIFarzVcbgfREFx8EcrnfodvKi8vR0FBAXr06IFWrWjuVVM7dOhQnb+nXGx9UoPPPTLV1dVx9epV6VbJq1ev4tChQ0hPT8eoUaM43WcSAJSVlXH//n3pDqoaT58+hZ6ensK9nKWrEHkvRkZG8Pf3x4ULF3h3cQKA8ePHY8aMGbUu1Xd2dmacrn75+fnYsWMH8vPz8dNPP0FPTw9//PEH9PX15VZ+sVZcXNzgz2pqajZhkn8nODgYkZGRmD59Ouso762yshKRkZGIi4uDlZWV3EMW1wojb2vfvj1KSkrQr18/2NnZwc3NDba2tryYHBgfHw8nJyeIRCJcv34dffv2xa1btyCRSDg57fPNQhPfm/jW5vDhwygoKOB0UYrPE7EUQbt27XjRa+9tNW/LR4wYgVOnTmH48OHIycnBw4cPMX78eNbx/jUuvjdfs2YNdHV1sWzZMt71OwSqV3i5u7tLX0bk5ORAJBLB3d0dXbp04V27Aj4ICwvDf//7X0yfPh1HjhzBjBkzkJ+fj0uXLmHBggWs49Vr27ZtUFdXl25TfRPXp1CrqqpKezTGxcVJVxa1b9/+vZ5TWJFIJLWuYr9z5w60tLQYJGpatFKKw7i4UorvDXz5ulQ/OTkZI0eOxKBBg3D69GlkZ2dDJBJhzZo1uHz5Mg4dOsQ6ogwlJaUGbwficqW/Y8eOOHPmDG+2wGVlZaFv375QUlLifQPc48ePw9bWltNFy7rY2Nhg5MiRCAwMlF7H9fT04OLiAkdHR8ybN491xDrxvYkvX/F5IpYi4sPqOqB64uScOXOwYMEC6bWme/fumDNnDjp16oTAwEDWEetUWVmJpKQk5OfnY/LkydDQ0MC9e/egqanJ6X47fO936OnpibNnz2L9+vVwdHREVlYWRCIRjhw5guXLl/Ni++HbuD6NrHfv3li2bBm+/vprmWe7gIAAPHv2DBs3bmQdUSE5OTmhvLwcgwYNwooVK1BQUCBt47Jw4ULk5OSwjlgrCwsLCAQCiMVi9OnTR2YlY1VVFQoKCuDo6IgDBw4wTNn4qCjFYVwsSikKvi3V/+STTzBx4kQsWrRI5ntx8eJFODs7c66x5ptvU27dugU/Pz9Mnz4dn3zyCQDg/Pnz2LVrF1avXs3p7UCrV6/G/fv3ERYWxjpKg7y51FckEuHSpUvQ0dFhHavF0dDQQGZmJnr06AGhUIiUlBT06dMHYrEYY8eOxa1bt1hHfCe+NvHlKz5PxFJEQ4cORUFBAedftLVr1w7/+7//C0NDQ+jo6CApKQlmZmbIzs7GkCFDcP/+fdYRa1VYWAhHR0cUFRXhn3/+ka7W8fT0xD///IPw8HDWERuMb/0ODQwM8Msvv2DgwIEy95N5eXmwtLTkxQqSt3F9Gpmamhqys7NhYGAAPT09/Pnnn+jXrx9yc3MxcOBAPH36lHVEhVRUVIT58+fj9u3b8PDwwKxZswBUt7eoqqri7L19zcuEwMBALF68WKZIr6qqCkNDQ0yYMAGqqqqsIjYJ2r7HYQ1daULeH9+W6l+5cgV79+6VO66np8fJhxU7Ozvpz0FBQQgJCcHXX38tPebk5AQzMzNs27aN00WpixcvIiEhAb///jv69Okjt12Va/06tLW1UVBQAD09Pdy6dQuvX79mHalFateunbRnRKdOnZCfny/dYsvF39faCIVC6OjoQCgUQltbG61atYKuri7rWPVavXo1PvroI8ycOVPmeGRkJB4/fszpqbHffvstXFxcpBOxavoenj59GmZmZmzDtUDx8fGsIzSIUCiU9sLs0qULrl69CjMzM7x48UK6bYWLPD09YW1tDbFYLPPiZPz48XBzc2OY7N3e1e+Q6x4/fizXowaoflnL1+eOqKgo1hHq1bFjRzx79gwGBgbQ19fHhQsX0K9fP2nfQK7jU4/MN+nr6+P333+XO871nqTLli0DABgaGuKrr75CmzZtGCdqHlSU4jCuXqj4enGqD9eX6mtra+P+/fty2yczMjLQpUsXRqka5vz587W+9bS2tsbs2bMZJGo4bW1tzvcae9OECRNgZ2eHTp06QSAQwNraGsrKyrV+lusrAPhs4MCBSElJgYmJCUaNGoXFixfjypUriImJwcCBA1nHq1dtTXz9/Px40cR369attRbv+/Tpg0mTJnG6KDV//nzY2NhIJ2IpKSkBAEQiEVauXMk4HeGqwYMH488//4SZmRkmTpwIT09PJCQk4M8//8TQoUNZx6vTmTNncO7cObk3/YaGhrh79y6jVA3D536HQPW91/Hjx+Hu7g7g/16A//zzz9LV7FzF12lkQ4YMwdGjR2FhYYEZM2bAy8sLhw4dwuXLlzl/j8m3Hplvq6qqwm+//Ybs7GwA1fcDTk5Odd4bc0nNS/vy8nI8evRI7kWzvr4+i1hNhrbvMcLXvfTvujhxvU9NXbi+VN/b2xupqak4ePAgevbsifT0dDx8+BCurq5wdXWVVtW5qFevXhg7dizWrVsnc9zHxwdHjhzBjRs3GCVTTLGxscjLy4OHhweCgoKgoaFR6+c8PT2bOVnLcfPmTZSUlMDc3BylpaVYvHixdGx1SEgIDAwMWEesk5KSEnR1deHl5cW7Jr5t2rRBdna2XPH+5s2bMDU1xatXrxglaziaiNX8cnNzkZiYWOtNP1dfVNV49uwZXr16hc6dO+P169dYt26d9FrzP//zP5wtJAuFQpw9exampqYyW8hSUlIwYcIEPHz4kHXEOvG53yEApKSkYOTIkZgyZQp27tyJOXPm4Nq1azh37hySk5NhZWXFOmKd+DqN7PXr13j9+rX0mr5//37p7+mcOXM4vQ2Lzz0y8/LyMGrUKNy9exe9evUCANy4cQPdunXD8ePHOT/MJTc3FzNnzsS5c+dkjtc0QOfq9/1DUVGKAT7vpefzxYnPysvLsWDBAuzcuRNVVVVo1aoVqqqqMHnyZOzcuZPTFf8TJ05gwoQJMDIywoABAwBUb4vLzc3F4cOHMWrUKMYJFdOMGTMQFhZWZ1GKsLdv3z44OTlxqqcdn5v4GhsbY9myZZgyZYrM8ejoaCxbtoyzLx0AmojFyvbt2zFv3jx06NABHTt2lNm+JBAIOD2q/X2sWbMGc+fO5cxqnq+++gpaWlrYtm0bNDQ0kJWVBV1dXYwdOxb6+vrYsWMH64gK7ebNm1i9ejXEYjFKSkpgaWkJX19fzm8VVlJSwsOHD+W2kovFYjg4OODZs2eMkikuPvfIHDVqFCQSCfbs2YP27dsDqC5gTpkyBUpKSjh+/DjjhPUbNGgQWrVqBT8/P+kOiDf169ePUbKmQUUpBsaNGwcNDQ1ERERAR0dH+oYoKSkJbm5uyM3NZR2xTny+OCmCoqIiXL16FSUlJbCwsODNNKw7d+5gy5Yt0uWzJiYmmDt3Lrp168Y4mTxLS0vEx8dDKBRKJ2DURVEeWAgbmpqayMzM5PQwCz418V23bh3WrVuHH374AUOGDAFQvbrXx8cHixcvxtKlSxknrJsiTsTiAwMDA8yfP5/TWzsbA9euNXfu3MGIESMgkUiQm5sLa2tr5ObmokOHDjh9+nStPY/Iv1dRUYE5c+bA39+/3mnaXNNSp5FxQceOHZGYmAgTExOYmppizZo1cHJyglgsxqBBgzg9GbZdu3a4cOGCXLGVD9mB6vxpaWno3bs36yjNgtaGM8DnvfSK0MD3xo0b2LBhg0yBZOHChbz4pdfX1+flHuKuXbvi+++/r/cz8+fPR1BQEDp06NBMqWo3duxYab+CsWPH8rbxJ+E+Lr4T4nMT3yVLluDp06eYP3++9O9UmzZt4Ovry+mCFAD89ttv0olYb15z+vTpI50USxrf8+fPMXHiRNYxmhzXrjVdu3aFWCzG/v37kZWVhZKSEsyaNQsuLi5o27Yt63gKS0VFBYcPH4a/vz/rKO9l3LhxAIDMzEyMGDGizmlkfGNiYoKcnBxOv+zhc4/M1q1bSwdBvKmkpITTWyZrmJqa8ubZujFQUYqBut4237lzh/Nbbfh8cQKAw4cPY9KkSbC2tpY2dKypou/fv5+zf9QkEgkOHTpUZ98Lrk2B+xC7d++Gt7c386LUm/25li9fzi4IIQzwrYlvVlYW+vbtCyUlJQgEAqxduxb+/v7Izs5G27ZtYWxsLNcUl4sUcSIWH0ycOBGnTp3C3LlzWUdpcVq1aiW31ZY0vXHjxuG3336Dl5cX6ygNpqjTyFavXo2//vqLdYx6hYSESFcUBQYGoqSkBL/88ou0RyaXffHFF/jmm28QEREBGxsbAEBqairmzp0LJycnxunebe3atfDx8cGqVatgZmYmNwGcr33t6kLb9xjg8156PjfwBYAePXrAxcUFQUFBMseXLVuG3bt3c/aNtKenJ7Zu3QoHBwd89NFHcg8pXP7ONNSbzU65QiQS4dKlSzIjqwHgxYsXsLS05HSPGsJ9XPzO862J75uNb+v6feWDwYMHY+LEiXB3d5feF3Tv3h3u7u7Izc1FbGws64gKafXq1QgJCcHo0aNrven38PBglKxxcfFac+/ePaSkpNT6ok1R/t25aOXKlQgODsbQoUNhZWUl19OQD//2LWUaGfl3Xrx4gWnTpuHYsWPSa3tlZSWcnJywc+dOaGlpMU5Yv5opvG8/81Gjc9JoaC89O2pqasjKyoKRkZHM8dzcXPTr1w9lZWWMktWvffv22L17t0I3BefiTbOSkhIePHgg9zv58OFDdOvWTbpFiJAPwcXvPN/o6OjgxIkTGDBgQJ1NcPmAzxOx+Ky+vjoCgUBhXjxw7VpT8x1XVVWFjo6OXIN5Rfl35yI+f+db2jQy0jhyc3Nx/fp1ANVbJt9+BuSq5OTkes9zvaXC+6LtewzQXnp27O3tcebMGbkLUkpKCmxtbRmlejctLS3O3Ey2BEePHpX+fPLkSZm3KVVVVYiPj+dVk1BCFNWECRNgZ2cnnUxjbW1d5zRSLj9sffbZZxCLxVi9ejXMzMxw6tQpWFpa4vz585yfiMVnBQUFrCO0SP7+/ggICMDSpUulqwFI8+Dzd3769Olo1aoVfv/991qnkXHJuwblvIlrQ3OEQmGDs/Nh4qGxsTFvBkO9SdGKTu9CRSlG+LSXXpEuTk5OTvD19UVaWpq0B9aFCxdw8OBBBAYGyhQjuLTfePny5QgMDERkZCQVLptBTVNNgUCAadOmyZxTUVGBoaEhgoODGSQjisTAwEBuuxB5P9u2bYOzszPy8vLg4eEBNzc3zvdmfNubE7G2b9/OOg5RQLa2tpy6dygrK8OkSZOoIMVYzWYZLhd33pSZmcmbaWQ195F8tH79etYRGsWiRYsa/Fmu9sc6c+YMtm7dips3b+LgwYPo0qULoqOj0b17d3z22Wes4zUq2r7HCJ8mwO3atavBn337AZ5rGnoDxLVlwC9fvsT48eNx9uxZGBoayj3Icu0ty4fg2vYCoHqZ+6VLl5g3Xyf8VVJSItf3gi/9mvhmxowZCAsL411RCqheDZuZmUkrMBm4c+cOjh49iqKiIrkt2Vx9UHnbo0ePau2xY25uzihR/Xx8fNC+fXv4+fmxjtIiRUREIDQ0FLm5uQCqV5J8++23mD17NuNk9fv4448RGhqqcA/jXObq6gp7e3vY2dmhR48erOO8FwcHB2RkZKCiogK9evUCAOTk5EBZWRmWlpbSzwkEAiQkJLCKWafDhw9j6tSpcHFxQXR0NK5duwaRSISNGzfixIkTOHHiBOuIjYqKUgzUNQHu0qVLnJ4AB/D74sRnX375JRITE/Gf//yn1kbnb06M46t58+ZhxYoVVAAivFdQUICFCxciKSkJr169kh6nvhekLtOmTUP//v15NRFLEcTHx8PJyQkikQjXr19H3759cevWLUgkElhaWnLyQeVNaWlpmDZtGrKzs2VWvXD9WlNVVYUvvvgCL1++rLXBPF+KgXwUEBCAkJAQuLu7S59Bzp8/j40bN8LLy0tuEBCXJCQk4H/+5394O40sLS1NuhihT58+sLCwYJzo3dzc3JCcnIz8/Hx07twZdnZ20udArm+JCwkJQVJSEnbt2gWhUAgAeP78OWbMmAFbW1ssXryYccL6WVhYwMvLC66urjIv7jMyMjBy5Eg8ePCAdcRGRUUpBvg6AQ7g98WJz9q1a4eTJ0/y+u1QWVlZrW+iufomtz6XL19GWVkZBg8ezDoK4aBBgwZBIpHA09Oz1iJyS+sTwNrmzZvx5MkTBAQEsI5SJ0WYiMVHNjY2GDlyJAIDA6U3/Xp6enBxcYGjoyPmzZvHOmK9+vXrhx49esDX17fWaw1XJyKvXLkSAQEB6NWrl1xurq5aUBS6uroICwvD119/LXN83759cHd3x5MnTxgleze+TiN79OgRJk2ahKSkJGhrawOongzn4OCA/fv382I4x927d3H69GkkJycjOTkZOTk56NSpE+7cucM6Wp26dOmCU6dOoU+fPjLHr169iuHDh+PevXuMkjWMmpoarl27BkNDQ5mi1M2bN2Fqairz0lMRUE8pBu7fvw9XV1e541OmTMEPP/zAIFHD1fS7ePPiFBwcjDlz5nD+4lSjtLQUycnJtRZIuHrj361bN86/AarL48ePMWPGDPzxxx+1nufqTUR9pk6dipycHF5mJ01PLBYjLS1NulycsHX48GEUFBRwuigVEREBbW1tpKWlIS0tTeacQCDg7N8mvsvOzsa+ffsAVPf6fPnyJdTV1REUFISxY8dyvih18+ZNHD58mDfTpGoEBwcjMjIS06dPZx2lxamoqIC1tbXccSsrK1RWVjJI1HCJiYmsI3wQd3d3/P333/jf//1fmJiYAACuXbuGadOmwcPDQ3oN4jKhUAgdHR0IhUJoa2ujVatWnC+mFRcX4/Hjx3LHHz9+jL///ptBovfTsWNH5OXlwdDQUOZ4SkoKp1qdNBYqSjHA1wlwb+LjxQkAMjIyMGrUKJSVlaG0tBTt27fHkydPoKamBj09Pc7e+AcHB8PHxwfh4eFyFyeu+/bbb/HixQukpqbC3t4ev/76Kx4+fChdGcBH8fHxqKioYB2DcNTHH3+M27dvU1GKI+Lj41lHeCc+T8Tis3bt2klfTnXq1An5+fnSt+pcXjFSY+jQoRCLxbwrSrVu3RqDBg1iHaNFmjp1KrZs2SK3RXLbtm1wcXFhlKph+LrKODY2FnFxcdKCFACYmppi06ZNGD58OMNk7/bdd98hKSkJGRkZMDExgZ2dHfz8/DB48GDpljiuGj9+PGbMmIHg4GDY2NgAAFJTU7FkyRI4OzszTvdubm5u8PT0RGRkJAQCAe7du4fz58/D29sb/v7+rOM1Otq+x0B4eDgCAgLw5Zdf1joBrnPnztLPcmkCHFD7xcne3p4XFyeguiDYs2dPhIeHQ0tLC2KxGCoqKpgyZQo8PT05e5ESCoUoKytDZWUl1NTU5PbRc3nqYadOnXDkyBHY2NhAU1MTly9fRs+ePXH06FGsW7cOKSkprCMS0qjy8/Mxd+5cTJkyBX379pX7feXjllXSfPg2EYvPxo0bh9GjR8PNzQ3e3t44cuQIpk+fjpiYGAiFQsTFxbGOWK8nT55g2rRpsLGxqfVaw7V7yBqrV6/G/fv3ERYWxjpKi+Pu7o6oqCh069ZN+gySmpqKoqIiuLq6ynyHuNjbi4/TyDQ0NHDmzBn0799f5nhGRgbs7OxQXFzMJlgDKCkpQVdXF15eXnB2dkbPnj1ZR2qwsrIyeHt7IzIyUvoiuVWrVpg1axZ++OEHuW3yXCORSLBq1SqsXr0aZWVlAKoL+t7e3lixYgXjdI2PilIM8HUCHMDvixMAaGtrIzU1Fb169YK2tjbOnz8PExMTpKamYtq0abh+/TrriLV61wRELk891NTURFZWFgwNDWFgYIC9e/di0KBBKCgoQJ8+faQXWq7Kz8/Hjh07kJ+fj59++gl6enr4448/oK+vL7dPnRCg+iXD5MmTcevWLekxPjQfVgS5ublITEysdRIZl7fvAfydiMVnN2/eRElJCczNzVFaWorFixfj3LlzMDY2RkhICGd7MtU4duwYpk6dWutDLZevNePHj0dCQgJ0dHTQp08fuWJaTEwMo2SKz8HBoUGf42JvL75OIxs7dixevHiBffv2SRce3L17Fy4uLhAKhfj1118ZJ6ybWCxGcnIykpKScObMGaiqqkoXJNS86Oe60tJSab/mHj16cL4Y9bby8nLk5eWhpKQEpqamUFdXZx2pSVBRirwXvl+cdHV1pTecPXv2xIYNGzBixAhcv34dVlZWKC0tZR1R4Xz88cdYuXIlRowYAScnJ2hra2P16tUICwvDoUOHON3YPzk5GSNHjsSgQYNw+vRpZGdnQyQSYc2aNbh8+TIOHTrEOiLhIFNTU5iYmMDHx4dXzYf5bvv27Zg3bx46dOiAjh07yjVPTk9PZ5iufnyeiEXYMTQ0xBdffAF/f3989NFHrOM02IwZM+o9v2PHjmZKQviEr9PIbt++DScnJ/zv//4vunXrJj3Wt29fHD16FF27dmWcsOHEYjFCQ0OxZ88evH79mrOFb0VUXFyMhIQE9OrVS2YrqKKgohQDN2/eVJgGZXy7OA0fPhzTp0/H5MmT4ebmhqysLHh4eCA6OhrPnz9Hamoq64jv9OrVK7kG7Vxugr57925UVlZi+vTpSEtLg6OjI549ewZVVVXs3LkTX331FeuIdfrkk08wceJELFq0SOYG6OLFi3B2duZFY3/S/Nq1a8fLPi98Z2BggPnz58PX15d1lPfG54lYhB0NDQ1kZmaiR48erKMQ0uT4PI1MIpEgLi5OuiPDxMQEn3/+OeNU7yaRSJCRkYGkpCQkJSUhJSUFxcXFMDc3h52dHUJDQ1lHfG98mMgLAF9++SUGDx6MhQsX4uXLl+jfvz8KCgogkUiwf/9+TJgwgXXERkWNzhkwMjKCnZ0dZs2ahf/85z9o06YN60gN9q6LE9etWrVKOnHh+++/h6urK+bNmwdjY2NEREQwTle30tJS+Pr64sCBA3j69KnceS4XA6dMmSL92crKCoWFhbh+/Tr09fXRoUMHhsne7cqVK9i7d6/ccT09PXpIJHUaMmQIFaUYeP78OSZOnMg6xgfh80QsvhEKhQ3u18Xlfo0A4OzsjMTERCpKkX+NDw/qfJxGVlFRgbZt2yIzMxPDhg3DsGHDWEd6L+3bt0dJSQn69esHOzs7uLm5wdbWFtra2qyjfTA+TOQFgNOnT+O///0vAODXX3/F69ev8eLFC+zatQsrV66kohT599LT07Fjxw4sWrQICxcuxFdffYVZs2ZJJwNwGd8vTm/e9Ovp6SE2NpZhmobz8fFBYmIitmzZgqlTp2LTpk24e/cutm7dijVr1rCOV6+goCB4e3tDTU0NQPWbLktLS7x8+RJBQUGc/qOgra2N+/fvo3v37jLHMzIy0KVLF0apCNeNGTMGXl5euHLlCszMzHjTfJjvJk6ciFOnTmHu3Lmso7w3Pk/E4pv169ezjtBoevbsiaVLlyIlJaXWaw2XJgpbWloiPj4eQqEQFhYW9RYGubzVVlHx4UGdj9PIVFRUoK+vz+mXx/XZvXs3bG1tOb0j433xYSIvAPz1119o3749gOoJjhMmTICamhpGjx6NJUuWME7X+Gj7HkOVlZU4evQodu7cidjYWPTs2RMzZ87E1KlToauryzperY4fP87ri9OQIUMQExMjV0QrLi7GuHHjONfUsYa+vj6ioqJgb28PTU1NpKenw8jICNHR0di3bx9nmzsCgLKyMu7fvw89PT2Z40+fPoWenh6n/1B7e3sjNTUVBw8eRM+ePZGeno6HDx/C1dUVrq6uWLZsGeuIhIPqG2bB5ebDfLd69WqEhIRg9OjRnH9AfxvfJ2LxlaurK+zt7WFnZ8fL1UZvvzB5k0AgwM2bN5sxTf0CAwOxZMkSqKmpYfny5fUWpehvK6kNX6eRRUREICYmBtHR0dIiAyHv0rNnT6xcuRKjR49G9+7dsX//fulK/KFDhyrcjg0qSnHAP//8g82bN2Pp0qUoLy+HqqoqvvzyS6xduxadOnViHU+hKCkp4cGDB3IFkkePHqFLly7SkaFco66ujmvXrkFfXx9du3ZFTEwMbGxsUFBQADMzM5SUlLCOWCclJSU8fPhQrtCakJCAr776Co8fP2aU7N3Ky8uxYMEC7Ny5E1VVVWjVqhWqqqowefJk7Ny5E8rKyqwjEkL+Pz49oL+NzxOx+MzNzQ3JycnIz89H586dpYNb7OzsYGxszDoeIaQWfJtGZmFhgby8PFRUVMDAwEBu+hutDGw6fJ7Iu3nzZnh6ekJdXR36+vrIyMiAkpISNmzYgJiYGCQmJrKO2KioKMXQ5cuXERkZif3796Ndu3aYNm0aZs2ahTt37iAwMBDFxcW4ePEi65gKISsrCwDQv39/JCQkyLypqKqqQmxsLLZu3Sozwp1LzM3NsWHDBtjZ2eHzzz9H//798eOPPyIsLAzr1q3jZMPtmr4df/31FzQ1NWXeilZVVaGkpARz587Fpk2bGKZsmKKiIly9ehUlJSWwsLCghxVCCFEgd+/exenTp5GcnIzk5GTk5OSgU6dOnPzbWpea2/mG9stiSSQS4dKlS9DR0ZE5/uLFC1haWnK6gKwIbty4gQ0bNiA7OxtAddPthQsXonfv3oyTvR++TCOjlYFs8Hkib43Lly/j9u3bGDZsmLT4evz4cWhra2PQoEGM0zUuKkoxEBISgh07duDGjRsYNWoUZs+ejVGjRsls+bhz5w4MDQ2pwWkjUVJSkl6MavvKt23bFhs2bMDMmTObO1qDhIaGQllZGR4eHoiLi8OYMWMgkUhQUVGBkJAQeHp6so4oZ9euXZBIJJg5cybWr18PLS0t6TlVVVUYGhpKR58Tomji4+MRGhoqc9P/7bff8mLaDiEtTVlZGVJSUpCYmIikpCSkp6fD1NQUGRkZrKO9U0REBEJDQ5GbmwsAMDY2xrfffovZs2czTla3ulatP3z4EN26dZObMEwaz+HDhzFp0iRYW1tL78EuXLiAS5cucX6iV0ubRkb+HT5P5H1TeXk5CgoK0KNHD7RqpbjtwKkoxYCxsTFmzpyJ6dOn17k9r7y8HPv27cO0adOaOZ1iKiwshEQigUgkwsWLF2W2kqmqqkJPT49XW7EKCwuRlpYGIyMjmJubs45Tr+TkZHz66ady/V34QCKR4NChQ3Uu/Y2JiWGUjHBZzZLr//znPzI3/YcOHUJoaCgWLFjAOKHiunPnDo4ePYqioiK5B1s+9mLiw0QsPvvuu++QlJSEjIwMmJiYSLfvDR48GEKhkHW8dwoICEBISAjc3d2l15rz589j48aN8PLyQlBQEOOEso4ePQoAGDduHHbt2iXzsqqqqgrx8fH4888/cePGDVYRFV6PHj3g4uIi991YtmwZdu/ejfz8fEbJ3q1jx444efIk+vXrh71792LZsmUQi8XYtWsXtm3bxtkiMq0MZENTUxOZmZmcncz4LmVlZXB3d8euXbsAADk5ORCJRHB3d0eXLl3g5+fHOGHjoqIUA7du3YK+vr5cM1yJRILbt29DX1+fUTLCRRUVFXB0dER4eDhvto0VFxc3+LNcbprv6emJrVu3wsHBAR999JHc8usdO3YwSka4rGvXrvDz88PChQtljm/atAmrVq3C3bt3GSVTbPHx8XBycoJIJML169fRt29f3Lp1CxKJBJaWlrzsxTR06FAUFBTQQ0sTUVJSgq6uLry8vODs7IyePXuyjvRedHV1ERYWhq+//lrm+L59++Du7s65Rrg1970CgUBu1bqKigoMDQ0RHByML774gkW8FkFNTQ1ZWVkwMjKSOZ6bm4t+/fpJG4hzUdu2bZGTk4Nu3brB1dUVnTt3xpo1a1BUVARTU1PO9lellYFszJo1Cx9//DEvJ/IC1c8gZ8+exfr16+Ho6IisrCyIRCIcOXIEy5cv52wR9kMp7howDuvRo0et08iePXuG7t2702SmJpafn4/169dLt9WYmprC09OTs5N3VFRUpD2x+EJbW/udfS0kEgnnJ5FFR0cjJiYGo0aNYh2F8MiLFy/g6Ogod3z48OG8X0bOZUuXLoW3tzcCAwOhoaGBw4cPQ09PDy4uLrX+/4MP+DK6mq8yMjKQnJyMpKQkBAcHQ1VVVbpayt7envNFqoqKClhbW8sdt7Ky4mT7h5rVxt27d8elS5fQoUMHxolaHnt7e5w5c0auKJWSkgJbW1tGqRqmW7duOH/+PNq3b4/Y2Fjs378fAPD8+XO0adOGcTp5NSsDAeDkyZO1rgysb0AH+XeMjIzg7++PCxcu8G4iLwD89ttv+OWXXzBw4ECZZ6o+ffpwekXjh6KVUgzUVTEvLCyEqakpSktLGSVTfCdPnoSTkxP69+8vbRB39uxZiMViHDt2DMOGDWOcsHZeXl5o3bo11qxZwzpKgyQnJzf4s3Z2dk2Y5N/p3r07/vjjD941/yRsTZ48GRYWFliyZInM8R9//BGXL1+W3kiTxqWhoYHMzEz06NEDQqEQKSkp6NOnD8RiMcaOHcvZQRaEO8RiMUJDQ7Fnzx68fv2a0y9NAMDd3R0qKipyW1O9vb3x8uVLXgwSIc0rPDwcAQEB+PLLLzFw4EAA1dvLDx48iMDAQHTu3Fn6WScnJ1Yxa8W3aWS0MpAtPk/kBapXNV69ehUikQgaGhoQi8UQiUQQi8UYPHgw/vrrL9YRGxWtlGpGixYtAlD9ixAQEAA1NTXpuaqqKqSmpqJ///6M0rUMfn5+8PLykivu+Pn5wdfXl7NFqcrKSkRGRiIuLg5WVlZy42S51iuFy4Wm97F8+XIEBgYiMjISbdu2ZR2HcFhYWJj0Z1NTU3z//fdISkqS6Sl19uxZLF68mFVEhdeuXTvpNohOnTohPz8fffr0AQDObWOqjaJMxOITiUSCjIwMJCUlISkpCSkpKSguLoa5uTln/47V3EsC1feTP//8M06dOiUtMKSmpqKoqAiurq6sIn6wy5cvo6ysDIMHD2YdRWHNnz8fQHWBZ/PmzbWeA8DJlezz58+HjY2NdBpZTdFHJBJh5cqVjNPJo5WBbBUUFLCO8K9YW1vj+PHjcHd3B/B/k1V//vlnhRwURSulmpGDgwOA6lUkn3zyCVRVVaXnaqaReXt786ZvEB+1adMGV65ckfs3zsnJgbm5OV69esUombysrCz07dsXSkpK0u9ObQQCAed7pZw5cwZbt27FzZs3cfDgQXTp0gXR0dHo3r07PvvsM9bx6vTy5UuMHz8eZ8+ehaGhodzSXz6MkyXNo6FL8Pnwdo6vxo0bh9GjR8PNzQ3e3t44cuQIpk+fjpiYGAiFQsTFxbGOWCc+T8TiM6FQiJKSEvTr10+6bc/W1hba2tqso9WpvvuBN/Hh3uBtJiYmyMnJ4VwxhHBLS5lGRlq2lJQUjBw5ElOmTMHOnTsxZ84cXLt2DefOnUNycjKsrKxYR2xU9JvcjGqWlc6YMQM//fQTpxs8KypdXV1kZmbKFaUyMzPltlOyZmFhIe09VlhYWOvkDj44fPgwpk6dChcXF6Snp+Off/4BAPz1119YtWoVTpw4wThh3aZNm4a0tDRMmTKl1kbnhNTg+xs5RRASEiJtdBsYGIiSkhL88ssvMDY25txq0rf5+Phg6dKltU7E8vHxoaJUE9m9ezdsbW15dT/GtS1KjSk+Ph4VFRWsYxCO4vM0svj4eMTHx9c6yTkyMpJRKsXH54m8n332GcRiMVavXg0zMzOcOnUKlpaWOH/+PMzMzFjHa3S0Uoq0KEFBQQgNDYWfnx8+/fRTANU9pdauXYtFixbB39+fccL/o6OjgxMnTmDAgAFQUlLCw4cPoauryzrWe7OwsICXlxdcXV1l9kRnZGRg5MiRePDgAeuIdWrXrh1OnjzJ6dVchBD+4/NELEIIv5SWliI5ObnWB3UuN3/m6zSywMBABAUFwdraGp06dZJ7wfnrr78ySqbY+DyRt6KiAnPmzIG/v3+LaYZPK6U4ZPPmzXjy5AkCAgJYR1FY/v7+0NDQQHBwMJYuXQoA6Ny5M5YvX865P8QTJkyAnZ2d9A+YtbU1lJWVa/0sl7cD3bhxo9b+EFpaWnjx4kXzB3oP3bp149UbdMLOm31e3oXrb+dI8+PzRCzSvJydnRv82ZiYmCZM8u/k5+djx44dyM/Px08//QQ9PT388ccf0NfXl/aCI40vIyMDo0aNQllZGUpLS9G+fXs8efIEampq0NPT49y98Jv4Oo0sPDwcO3fuxNSpU1lHaVH4PJFXRUUFhw8f5tRiiaZGRSkOOXz4MAoKCqgo1YQEAgG8vLzg5eWFv//+G0D1xCYu2rZtG5ydnZGXlwcPDw+4ublxNmt9OnbsiLy8PBgaGsocT0lJgUgkYhOqgYKDg+Hj44Pw8HC5/IS86e03tOnp6aisrESvXr0AVG8zUFZWVrgeAKwJhcIGb6t99uxZE6f5cE5OTvD19UVaWlqtE7HeHC3OtYlYpHm9OVZeIpHg119/hZaWFqytrQEAaWlpePHixXsVr5pbcnIyRo4ciUGDBuH06dP4/vvvoaenB7FYjIiICBw6dIh1RIXl5eWFMWPGIDw8HFpaWrhw4QJUVFQwZcoUeHp6so5Xr8ePH9faaqO0tJTT7RXKy8uluzNI88nOzsa+ffsAAK1atcLLly+hrq6OoKAgjB07FvPmzWOcsH7jxo3Db7/9Bi8vL9ZRmgUVpTgkPj6edYQWhQ8FnppKflpaGjw9PXmR+W1ubm7w9PREZGQkBAIB7t27h/Pnz8Pb25vzbwCmTJmCsrIy9OjRA2pqanKNzrn8kEua15t9XkJCQqChoYFdu3ZBKBQCAJ4/f44ZM2bQqpdGtn79etYRGgWfJ2KR5rVjxw7pz76+vvjyyy8RHh4uXUldVVWF+fPnc3qVr5+fH1auXIlFixbJ3NcMGTIEGzduZJhM8WVmZmLr1q1QUlKCsrIy/vnnH4hEIqxbtw7Tpk3jdDGTr9PIZs+ejb1793L+nlfR8H0ir7GxMYKCgnD27NlaJ69zeVXjh6CeUkThWVpaIj4+HkKhEBYWFvW+TaFpao1PIpFg1apVWL16tbQvSuvWreHt7Y0VK1YwTle/mmaadZk2bVozJSF80qVLF5w6dUpuC8rVq1cxfPhw3Lt3j1Eyxebq6gp7e3vY2dmhR48erOMQ0uR0dXWRkpIiXZFZ48aNG/j000/x9OlTRsnqp66ujitXrqB79+4yvSZv3bqF3r17c2oSsqLR1dXFuXPnYGxsjJ49e2LDhg0YMWIErl+/DisrK5SWlrKOWCe+TiPz9PREVFQUzM3NYW5uLveCk7b0Nw0+T+QF6p/qrIiTnGmlFAOrV6/GRx99hJkzZ8ocj4yMxOPHj+Hr68somWIaO3YsWrduDaD6AkWal0AgwH//+18sWbIEeXl5KCkpgampKdTV1VlHeycqOpEPUVxcjMePH8sdf/z4sXTbMGl8rVu3xpo1a+Dm5obOnTvDzs5OWqR6e+IqIYqgsrIS169flytKXb9+XW7CF5doa2vj/v37cg9dGRkZ6NKlC6NULYOFhQUuXboEY2Nj2NnZISAgAE+ePEF0dDT69u3LOl69+DqNLCsrC/379wdQ/XLqTVzedsh3fJ7IC7S8qc60UooBQ0ND7N27V25/cWpqKiZNmtTivoSE8MGrV6/kptRweXsEYcfV1RVnzpxBcHAwbGxsAFRf35csWQJbW9t3rsAj/87du3dx+vRpJCcnIzk5GTk5OejUqRPu3LnDOlq9+DoRi7CzaNEiREVF4bvvvpO51qxZswZTp07l7IOXt7c3UlNTcfDgQfTs2RPp6el4+PAhXF1d4erqimXLlrGOqLAuX76Mv//+Gw4ODnj06BFcXV2lK6ciIiKkxROuaYnTyAipUVOuUeQiJhWlGGjTpg2ys7PlLqo3b96EqakpLVsmvKco04FKS0vh6+uLAwcO1LoNgnq7kNqUlZXB29sbkZGRqKioAFDdZHPWrFn44Ycf5PoCkMZVVlaGlJQUJCYmIikpCenp6TA1NeXsuHDg3ROxFG2ZPmkcr1+/xo8//oiffvoJ9+/fB1DdO8XT0xOLFy+uc2Iva+Xl5ViwYAF27tyJqqoqtGrVClVVVZg8eTJ27tzJ2dyELS0tLWRmZvK6KFXzcqRr166MkxA+iIiIQGhoKHJzcwFU95n69ttvMXv2bMbJGp8S6wAtUbdu3XD27Fm542fPnkXnzp0ZJFJsQqEQ7du3b9D/SOPQ0tKS/k9TUxPx8fG4fPmy9HxaWhri4+NlpghxkY+PDxISErBlyxa0bt0aP//8MwIDA9G5c2dERUWxjkc4Sk1NDZs3b8bTp0+RkZGBjIwMPHv2DJs3b6aCVBP67rvv8Omnn0JHRwd+fn549eoV/Pz88ODBA04XpID/m4j1/PlztG3bFhcuXEBhYSGsrKzw448/so5HOEpJSQk+Pj64e/cuXrx4gRcvXuDu3bvw8fHhdGFHVVUV27dvR35+Pn7//Xfs3r0b169fR3R0NKdzK4IhQ4bgxYsXcseLi4sxZMiQ5g/0HmqmkfHN69evERQUBC0tLRgYGMDAwADa2tpYsWIFp7fZ8pEiPfMFBATA09MTY8aMwcGDB3Hw4EGMGTMGXl5eCAgIYB2v0dFKKQbWrVuHdevW4YcffpD+AYiPj4ePjw8WL16MpUuXMk6oWN7cKvP06VOsXLkSI0aMkE7qOH/+PE6ePAl/f/8WM3azOfn6+uLZs2d1Tgf64YcfGCesm76+PqKiomBvbw9NTU2kp6fDyMgI0dHR2LdvH06cOME6IiHk/1NSUoKuri68vLzg7OyMnj17so7UYNra2khNTUWvXr2gra2N8+fPw8TEBKmpqZg2bRquX7/OOiIhRAEoKSnhwYMH0NPTkzn+6NEjdOnSRbq6l4tWrlyJ4OBgDB06lFfTyJYuXYqIiAgEBgZi0KBBAKqbti9fvhxubm74/vvvGSdUHO/THoHrfWN1dXURFhaGr7/+Wub4vn374O7uzosJgu+DilIMSCQS+Pn5ISwsTNo3ok2bNvD19VXIyieXTJgwAQ4ODli4cKHM8Y0bNyIuLo6Xb2C4jq/TgYDqCUHXrl2Dvr4+unbtipiYGNjY2KCgoABmZmbSBoqENMTmzZvx5MkTus43EbFYjOTkZCQlJeHMmTNQVVWVNju3t7fndJGKzxOxCPd89913ePDgASIjI1lHqZVEIsGhQ4eQmJiIR48eya0W4fK2fr7KysoCAPTv3x8JCQkyK0WqqqoQGxuLrVu34tatW4wSvhtfp5F17twZ4eHhcHJykjl+5MgRzJ8/H3fv3mWUTLHxfSKvtra2dCjBm3JycmBjY1Prikc+o+l7zSQrKwt9+/aFkpISBAIB1q5dC39/f2RnZ6Nt27YwNjaWTogjTefkyZNYu3at3HFHR0f4+fkxSKT4+DodCABEIhEKCgqgr6+P3r1748CBA7CxscGxY8egra3NOh7hmcOHD6OgoICKUk2kX79+6Nevn/RtuVgsRmhoKBYsWIDXr19zugccnydiEe65c+cObt++zTpGnb799lts3boVDg4O+OijjxS6eS9X9O/fHwKBAAKBoNZtem3btsWGDRsYJGs4vg6CevbsGXr37i13vHfv3nj27BmDRC0D3yfyTp06FVu2bJEbWLFt2za4uLgwStV0aKVUM1FWVsb9+/ehp6cHkUiES5cuQUdHh3WsFsfAwAAeHh5YvHixzPHg4GCEhYWhsLCQUTLFxdfpQAAQGhoKZWVleHh4IC4uDmPGjIFEIkFFRQVCQkLg6enJOiIh5P+TSCTIyMhAUlISkpKSkJKSguLiYpibm8POzg6hoaGsI9aJrxOxCPkQ7du3x+7duzFq1CjWUVqMwsJCSCQSiEQiXLx4Ebq6utJzqqqq0NPT41U/Lz5NIxswYAAGDBiAsLAwmePu7u64dOkSLly4wChZy8DXibzu7u6IiopCt27dMHDgQADVz09FRUVwdXWFioqK9LNcfpZqKCpKNRMdHR2cOHECAwYMgJKSEh4+fCjzB4E0j507d2L27NkYOXIkBgwYAKD6Fzw2Nhbbt2/H9OnT2QZUQHydDlSbwsJCpKWlwcjICObm5qzjEELeIBQKUVJSgn79+knfiNra2tKqRqKwoqKi8NVXX8mttC8vL8f+/fvh6urKKFn9unfvjj/++KPW1SOE1IeP08iSk5MxevRo6Ovry/SzvX37Nk6cOAFbW1vGCRUbHyfyAoCDg0ODPicQCJCQkNDEaZoeFaWayTfffIOoqCh06tQJRUVF6Nq1a50P41zdE60oUlNTERYWhuzsbACAiYkJPDw8pEUq0nSKi4sBAJqamnLnzp49C2tra85sY62oqICjoyPCw8N5scyXcMuNGzewYcMGmevMwoUL6SGsCR0/fhy2tra1Xl+4bsiQIYiJiZEroBUXF2PcuHEKccNJGt+bq/Df9PTpU+jp6XF2y+quXbsQGxuLyMhItG3blnWcFic/Px/r16+X/n0yNTWFp6cn5/vuBAQEICQkBO7u7jLFnY0bN8LLywtBQUGME9bt7t272Lx5s3RohYmJCebPn09T15vQd999h6SkJGRkZMDExET6smrw4MEQCoWs45G3UFGqGcXGxiIvLw8eHh4ICgqChoZGrZ+jLUGkJdLU1ERmZiZEIhHrKFJvNh8mpKEOHz6MSZMmwdraWnrjfOHCBVy6dAn79+/HhAkTGCckXMPniViEnbpW3ovFYjg4OHC2X83Lly8xfvx4nD17FoaGhjLbUAAgPT2dUTLFd/LkSTg5OaF///7SSXBnz56FWCzGsWPHMGzYMMYJ69bSppGRf4fPE3lbImp03owcHR0BAGlpafD09KyzKEWax6tXr6TTD2vw8Q27ouBifXzKlCmIiIjAmjVrWEchPOLj44OlS5fKvbVdtmwZfHx8qChFpGomYgHAtWvX8ODBA+l/10zE6tKlC4tohMMsLCykTauHDh2KVq3+73a+qqoKBQUF0ntOLpo2bRrS0tIwZcoUanTezPz8/ODl5SV3X+Pn5wdfX19OF6UqKipgbW0td9zKygqVlZUMEjXMjh07oK6ujokTJ8ocP3jwIMrKyjBt2jRGyRRbRkaGdCJvcHAwryby1kdRJznTSinSopSVlcHHxwcHDhzA06dP5c5zdal7S6ChoQGxWMyplVI1TQaNjY1hZWWFdu3ayZxXhMaCpPGpqakhKysLRkZGMsdzc3PRr18/lJWVMUpGuKZmIi9Qe2G+ZiLWzJkzmzsa4bDAwEDp/128eDHU1dWl51RVVWFoaIgJEyZAVVWVVcR6tWvXDidPnsRnn33GOkqL06ZNG1y5cqXWMfPm5uZ49eoVo2Tv5u7uDhUVFbl7L29vb7x8+RKbNm1ilKx+PXv2lE6bfFNycjK++eYb3Lhxg1GylqVmIu+ePXs4P5G3PkOHDkVBQYHCtfuhlVIcoqiVTy5ZsmQJEhMTsWXLFkydOhWbNm3C3bt3sXXrVloNQwBUr1zo27cvlJSUcPXqVVhaWgKovmF7E73ZJXWxt7fHmTNn5IpSKSkp1NCUyCgoKFCoiVikeSxbtgwAYGhoiK+++gpt2rRhnOj9dOvWjVamM6Krq4vMzEy5olRmZqbc9mEuioiIwKlTp2qdRrZo0SLp57j00rCoqAjdu3eXO25gYICioiIGiVqGd03k5av4+HjWEZoEFaU45PDhwygoKKCiVBM6duwYoqKiYG9vjxkzZsDW1hZGRkYwMDDAnj174OLiwjoiYczCwkLaOLawsBCXLl2Cjo4O61iER5ycnODr64u0tDTpjfOFCxdw8OBBBAYG4ujRozKfJS2XgYEBgOoppYS8r5ptP+Xl5Xj06JHc90hfX59FrHcKDg6Gj48PwsPDYWhoyDpOi+Lm5oZvvvkGN2/exKeffgqguqfU2rVrZYo6XPTmi8L8/HwAQIcOHdChQwdcvXpV+jmuvTTU09NDVlaW3HddLBbT/WUTat++vcxEXjc3N5rIy2G0fY+0KOrq6rh27Rr09fXRtWtXxMTEwMbGBgUFBTAzM0NJSQnriC0WVxqd6+jo4MSJExgwYECdTWQJqY+SklKDPicQCHi7fJw0Pr5OxCLs5ObmYubMmTh37pzMcYlEwunri1AoRFlZGSorK6GmpibX6JyrDdoVgUQiwfr16xEcHIx79+4BADp37owlS5bAw8ODcwUdReDr64tffvkFO3bswODBgwFUb92bOXMm/vOf/+DHH39knFAx8Xkib42WNMmZVkqRFkUkEqGgoAD6+vro3bs3Dhw4ABsbGxw7dowq54xxpT4+YcIE2NnZoVOnThAIBLC2tq5z+4yi7ecmjYNWvZD3VddErD59+nB+IhZhZ/r06WjVqhV+//136d8sPli/fj3rCC2WQCCAl5cXvLy88PfffwMADV5qYitWrMCtW7dkhhK8fv0arq6uWLVqFeN0imv06NGsI/wrdU1yNjMzU8hJzrRSipHc3FwkJibWutyatu81ndDQUCgrK8PDwwNxcXEYM2YMJBIJKioqEBISAk9PT9YRFVJlZSWSkpKQn5+PyZMnQ0NDA/fu3YOmpqZMg1auiI2NRV5eHjw8PBAUFFTnDRt9XwghjcHCwgIjRoyodSLWqVOnkJ6ezigZ4bJ27dohLS1NId+aE9JQfOnJm5ubi8zMTLRt2xZmZmbS7duE1KZHjx5wcXGpdZLz7t27pVtYFQUVpRjYvn075s2bhw4dOqBjx44yb7YEAgHdfDaRiooKODo6Ijw8XNrgsbCwEGlpaTAyMoK5uTnjhIqpsLAQjo6OKCoqwj///IOcnByIRCJ4enrin3/+QXh4OOuIdZoxYwbCwsLoLSJ5b6WlpUhOTkZRURHKy8tlznl4eDBKRbiKzxOxCDsff/wxQkNDeT3F7tWrV3LXSD5vt+EiS0tLxMfHQygUwsLCot4VdXx8BlHUaWSkZWtpk5xp+x4DK1euxPfffw9fX1/WUVoUFRUVZGVlyRwzMDCgNxVNzNPTE9bW1nINHcePHw83NzeGyd5tx44drCMQHsrIyMCoUaNQVlaG0tJStG/fHk+ePIGamhr09PSoKEXk8H0iFmFj7dq18PHxwapVq2BmZibXm4mrxZ3S0lL4+vriwIEDePr0qdx5rvbC4quxY8eidevWAIBx48axDdME+DqN7MiRI/jrr7/g6urKOgrhoJY2yZlWSjHAlYbOLZGXlxdat24tt0WCNB0dHR2cO3cOvXr1goaGBsRiMUQiEW7dugVTU1OFq/QTYm9vj549eyI8PBxaWloQi8VQUVHBlClT4OnpCWdnZ9YRCccEBQUhNDQUfn5+tU7E8vf3Z5yQcFHNUIW3V75wvdH5ggULkJiYiBUrVmDq1KnYtGkT7t69i61bt2LNmjU0CZm0CL1790Zubi5nf08JW+Hh4QgICMCXX35Z6yTnzp07Sz+rCJOcqSjFwKxZs/Dxxx9j7ty5rKO0OO7u7oiKioKxsTGsrKzQrl07mfMhISGMkikuoVCIs2fPwtTUVKYolZKSggkTJuDhw4esIxLSqLS1tZGamopevXpBW1sb58+fh4mJCVJTUzFt2jRcv36ddUTCMTQRi3yI5OTkes/b2dk1U5L3o6+vj6ioKNjb20NTUxPp6ekwMjJCdHQ09u3bhxMnTrCOSDiqJU0jIy1bS5vkTNv3GDAyMoK/v7+0g/7by61pa0fTuXr1KiwtLQFU9+p4E930N43hw4dj/fr12LZtG4Dqf+eSkhIsW7YMo0aNYpyOkManoqIivZnQ09NDUVERTExMoKWlhdu3bzNOR7iIJmKRD8HVotO7PHv2TLpbQFNTE8+ePQMAfPbZZ5g3bx7LaApJKBQ2+B635v8XXNTSppGRlq2lTXKmlVIMdO/evc5zAoGAGvURhXLnzh2MGDECEokEubm5sLa2Rm5uLjp06IDTp09TvxSicIYPH47p06dj8uTJcHNzQ1ZWFjw8PBAdHY3nz58jNTWVdURCiII4c+YMtm7dips3b+LgwYPo0qULoqOj0b17d842QDc3N8eGDRtgZ2eHzz//HP3798ePP/6IsLAwrFu3Dnfu3GEdUaHs2rVL+vPTp0+xcuVKjBgxQlrYOX/+PE6ePAl/f394eXmxivlOfJ9GVlZWVuvwExq0RAgVpQghzaCyshL79+9HVlYWSkpKYGlpCRcXF7Rt25Z1NEIa3eXLl/H333/DwcEBjx49gqurK86dOwdjY2NERESgf//+rCMSDlD0iVik6R0+fBhTp06Fi4sLoqOjce3aNYhEImzcuBEnTpzg7Da40NBQKCsrw8PDA3FxcRgzZgwkEgkqKioQEhICT09P1hEV1oQJE+Dg4ICFCxfKHN+4cSPi4uLw22+/sQnWAHydRvb48WPMmDEDf/zxR63nFWHrFWkaLWmSMxWlCCGEEEKaWWBgIJYsWQI1NTUEBgbW+9lly5Y1UyrCJxYWFvDy8oKrq6tMz8aMjAyMHDkSDx48YB2xQQoLC5GWlgYjIyNaNdLE1NXVkZmZKVfYycvLQ//+/VFSUsIo2buNGjUKEydOxIwZM2SO79ixA/v378fJkycZJaufi4sLCgsLsX79etjb2+PXX3/Fw4cPsXLlSgQHB2P06NGsIxIOetckZ0XbWUU9pRi5c+cOjh49Wmvlk5ptE0Vz7949pKSk4NGjR3J7pBWt0k/IkCFDEBMTA21tbZnjxcXFGDduHBISEtgEI5zyZqGJik7kQ9y4cQODBw+WO66lpYUXL140f6AGqKiogKOjI8LDw2FsbAwAMDAwgIGBAeNkLYOOjg6OHDmCxYsXyxw/cuQIdHR0GKVqGCcnJ/j6+iItLa3WaWRHjx6V+SxXJCQk4MiRI7C2toaSkhIMDAwwbNgwaGpqYvXq1VSUIrXy8vLCmDFjpJOcL1y4IDPJWdFQUYqB+Ph4ODk5QSQS4fr16+jbty9u3boFiUQibcJNiKLYuXMn5syZA1VVVejo6MhsUREIBFSUIgonKSlJ7mUDALx69QpnzpxhkIgQoog6duyIvLw8GBoayhxPSUmRNhLnGhUVFWRlZbGO0WIFBgZi9uzZSEpKwoABAwAAqampiI2Nxfbt2xmnq9/8+fMBAJs3b8bmzZtrPQdwbxpZaWmptH+qUCjE48eP0bNnT5iZmdHWbFKnzMxMbN26FUpKSlBWVsY///wDkUiEdevWYdq0aXB2dmYdsVFRUYqBpUuXwtvbG4GBgdDQ0MDhw4ehp6cHFxcXODo6so5HSKPy9/dHQEAAli5d2uDxpoTw0ZsPWteuXZPZOlNVVYXY2Fh06dKFRTTCQYoyEYuw4+bmBk9PT0RGRkIgEODevXs4f/48vL294e/vzzpenaZMmYKIiAisWbOGdZQWZ/r06TAxMUFYWBhiYmIAACYmJkhJSZEWqbiKr9PIevXqhRs3bsDQ0BD9+vXD1q1bYWhoiPDwcHTq1Il1PMJRLW2SMxWlGMjOzsa+ffsAAK1atcLLly+hrq6OoKAgjB07lsbhEoVSVlaGSZMmUUGKKLz+/ftDIBBAIBBgyJAhcufbtm2LDRs2MEhGuGj9+vXSn981EYuQ2vj5+eH169cYOnQoysrKMHjwYLRu3Rre3t5wd3dnHa9OlZWViIyMRFxcHKysrNCuXTuZ89TGomkNGDAAe/bsYR2jxfD09MT9+/cBVG/VdnR0xJ49e6CqqoqdO3eyDUc4y8LCApcuXYKxsTHs7OwQEBCAJ0+eIDo6Gn379mUdr9FRo3MGOnbsiMTERJiYmMDU1BRr1qyBk5MTxGIxBg0axOkmg4S8Lx8fH7Rv3x5+fn6soxDSpAoLCyGRSCASiXDx4kXo6upKz6mqqkJPTw/KysoMExKu4vNELMJeeXk58vLyUFJSAlNTU6irq7OOJCcrKwt9+/aFkpISHBwc6vycQCCgvnvN5NWrV3JbzTU1NRmlaRhFmEZWVlaG69evQ19fHx06dGAdh3BUS5vkTEUpBsaNG4fRo0fDzc0N3t7eOHLkCKZPn46YmBgIhULExcWxjkhIo6mqqsIXX3yBly9fwszMDCoqKjLn6Y0oIaSl4/NELMIdxcXFSEhIQK9evWBiYsI6jgxlZWXcv38fenp6EIlEuHTpEucbayuisrIy+Pj44MCBA3j69KnceS71YnqbIkwjq3nsbujWbUJaCtpPw0BISIh033ZgYCCGDh2KX375BYaGhoiIiGCcjpDGtXr1apw8eRIPHz7ElStXkJGRIf1fZmYm63iENIn8/Hy4u7vj888/x+effw4PDw/k5+ezjkU4qmYi1tv4MBGLsPPll19i48aNAICXL1/i448/xpdffglzc3McPnyYcTpZ2traKCgoAADcunWLt/2B+G7JkiVISEjAli1b0Lp1a/z8888IDAxE586dERUVxTpevWqmkT1//hxt27bFhQsXUFhYCCsrK/z444+s49UrIiICffv2RZs2bdCmTRv07dsXP//8M+tYhMOGDBlS6xTV4uLiWltE8B2tlCKENCmhUIjQ0FBMnz6ddRRCmsXJkyfh5OSE/v37Y9CgQQCAs2fPQiwW49ixYxg2bBjjhIRrdu7cidmzZ2PkyJG1TsSi6yepTceOHXHy5En069cPe/fuxbJlyyAWi7Fr1y5s27YNGRkZrCNKffPNN4iKikKnTp1QVFSErl271rmdmQ8rXvhKX18fUVFRsLe3h6amJtLT02FkZITo6Gjs27cPJ06cYB2xTtra2khNTUWvXr2gra2N8+fPw8TEBKmpqZg2bRquX7/OOmKtAgICEBISAnd3d5megRs3boSXlxeCgoIYJyRcpKSkhAcPHkgnN9Z49OgRunTpgoqKCkbJmgY1OieENKnWrVtLH8wJaQn8/Pzg5eUlN1nKz88Pvr6+VJQicvg8EYuw89dff6F9+/YAgNjYWEyYMAFqamoYPXo0lixZwjidrG3btsHZ2Rl5eXnw8PCAm5sbNDQ0WMdqcZ49ewaRSASgun9UzWTPzz77jPODlvg6jWzLli3Yvn07vv76a+kxJycnmJubw93dnYpSREZLneRMRalmQqOfSUvl6emJDRs2ICwsjHUUQppFdnY2Dhw4IHd85syZMhPXCHkTTcQi76tbt244f/482rdvj9jYWOzfvx8A8Pz5c7Rp04ZxOnmOjo4AgLS0NHh6elJRigGRSISCggLo6+ujd+/eOHDgAGxsbHDs2DFoa2uzjlcvvk4jq6iogLW1tdxxKysrVFZWMkhEuKylTnKmolQzoQcR0lJdvHgRCQkJ+P3339GnTx+5Ruc1qwIIURS6urrIzMyEsbGxzPHMzEy5ZdiEvI2PE7EIG99++y1cXFygrq4OfX192NvbAwBOnz4NMzMztuHqsWPHDtYRWqwZM2ZALBbDzs4Ofn5+GDNmDDZu3IiKigrOD55ZtWoV/v77bwDA999/D1dXV8ybN086jYyrpk6dii1btsj9+27btg0uLi6MUhGuKigoaJGTnKmnFAOurq6wt7eHnZ0devTowToOIU1qxowZ9Z6nm1OiaIKCghAaGgo/Pz98+umnAKp7Sq1duxaLFi2Cv78/44SEa/g8EYuwdfnyZdy+fRvDhg2Duro6AOD48ePQ1tamrfNERkVFBRwdHREeHi59aVJYWIi0tDQYGRnB3NyccULFsWjRIunPlZWV2LlzJ/T19TFw4EAA1T0Di4qK4OrqqpCrXgh5X1SUYsDNzQ3JycnIz89H586dYWdnJy1Svf1mnRBCCL9IJBKsX78ewcHBuHfvHgCgc+fOWLJkCTw8PGgUNJGzYMECJCYmYsWKFZg6dSo2bdqEu3fvYuvWrVizZg29TSf1Ki8vR0FBAXr06IFWrWgTBKmbrq4uzp07x8vnjSFDhiAmJkZum2FxcTHGjRuHhIQENsFq4eDg0KDPCQQCTuUm3JKfn4/169cjOzsbAGBqagpPT0+FXNRCRSmG7t69i9OnTyM5ORnJycnIyclBp06dcOfOHdbRCCGENIKarQbUO4XUh88TsQg7ZWVlcHd3x65duwAAOTk5EIlEcHd3R5cuXeDn58c4IeEaLy8vtG7dWm4QBx+0tGlkpGVraZOc6XUKQ0KhEDo6OhAKhdDW1karVq1k9o0SwleWlpaIj4+HUCiEhYVFvStD0tPTmzEZIc2LilGkIfg8EYuws3TpUojFYiQlJUmbiAPA559/juXLl1NRisiprKxEZGQk4uLiYGVlhXbt2smc52JfqZY6jYy0bC1tkjMVpRj47rvvkJSUhIyMDJiYmEibDQ4ePBhCoZB1PEL+tbFjx6J169bSn2m7ElF0VIgl/wafJ2IRdn777Tf88ssvGDhwoMw1p0+fPsjPz2eYjHDV1atXYWlpCaB6Zd2buHqvpqjTyDZv3ownT54gICCAdRTCQS1tkjMVpRhYs2YNdHV1sWzZMjg7O6Nnz56sIxHSqJYtWyb9efny5eyCENJM3izEjhs3jm0Ywjt8nohF2Hn8+HGtEz1LS0s5W2AgbCUmJrKO8N4UdRrZ4cOHUVBQQEUpUquWNsmZekoxIBaLkZycjKSkJJw5cwaqqqrSZuf29vZUpCIKRSQS4dKlS9DR0ZE5/uLFC1haWuLmzZuMkhFCCHs0EYt8qMGDB2PixIlwd3eHhoYGsrKy0L17d7i7uyM3NxexsbGsIxJCCPkALW2SMxWlOEAsFiM0NBR79uzB69evafQzUSh1NaZ8+PAhunXrhvLyckbJCCGEG/g8EYuwk5KSgpEjR2LKlCnYuXMn5syZg2vXruHcuXNITk6GlZUV64iENKqWNI2MtGwtbZIzFaUYkEgkyMjIQFJSEpKSkpCSkoLi4mKYm5vDzs4OoaGhrCMS8q8dPXoUQPVWpl27dkFLS0t6rqqqCvHx8fjzzz9x48YNVhEJaTRCobDBNwg1TawJqcHniViErZs3b2L16tUQi8UoKSmBpaUlfH19YWZmxjoaIY2Kz9PIcnNzkZiYiEePHuH169cy52j7HnmXljDJmYpSDAiFQpSUlKBfv37SbXu2trbUzJQoFCUlJQDVjTPfvsyoqKjA0NAQwcHB+OKLL1jEI6RR1YxkB4CnT59i5cqVGDFiBD755BMAwPnz53Hy5En4+/vDy8uLVUzCUe7u7oiKioKxsTFvJmIRtioqKjBnzhz4+/uje/furOMQ0uQsLCwwYsSIWqeRnTp1irNDRLZv34558+ahQ4cO6Nixo8wLLIFAwNnchDQnKkoxcPz4cdja2kJTU5N1FEKaXPfu3XHp0iV06NCBdRRCmsWECRPg4OCAhQsXyhzfuHEj4uLi8Ntvv7EJRjjLwcGhznMCgQAJCQnNmIbwhZaWFjIzM6koRVqENm3a4MqVK3LbnHNycmBubo5Xr14xSlY/AwMDzJ8/H76+vqyjEI5ryZOcafoeA6NHj2YdgZBmU1BQwDoCIc3q5MmTWLt2rdxxR0dH+Pn5MUhEuI6PE7EIe+PGjcNvv/1Gqy9Ji8DXaWTPnz/HxIkTWccgPNCSJzlTUYoQwsTly5dRVlaGwYMHs45CSKPS0dHBkSNHsHjxYpnjR44ckZtCSQghH8rY2BhBQUE4e/Zsrds+PTw8GCUjpPG5ubnhm2++wc2bN2udRsZVEydOxKlTpzB37lzWUQjHLVu2rNafWwLavkcIYcLExAQ5OTk0bZIonJ07d2L27NkYOXIkBgwYAABITU1FbGwstm/fjunTp7MNSAhRCPVt2xMIBLh582YzpiGkafF1Gtnq1asREhKC0aNHw8zMDCoqKjLnqXhMCBWlCCGM3Lt3DxUVFTAwMGAdhZBGl5qairCwMOnYahMTE3h4eEiLVIQQQgj5MHyaRkbFY9JQLXmSMxWlCCGEEEII4bGa23murhYhhBBSv5Y8yZmKUoSQJpefn48dO3YgPz8fP/30E/T09PDHH39AX18fffr0YR2PkCbz6tUrlJeXyxyjyauEkMYSERGB0NBQ5ObmAqjuM/Xtt99i9uzZjJMR8u+15GlkpGVraZOcqdE5IaRJJScnY+TIkRg0aBBOnz6N77//Hnp6ehCLxYiIiMChQ4dYRySkUZWVlcHHxwcHDhzA06dP5c5THzVCSGMICAhASEgI3N3dZd6ke3l5oaioCEFBQYwTEvLvKMo0sjt37uDo0aMoKiqSe1EVEhLCKBXhspY2yZlWShFCmtQnn3yCiRMnYtGiRdDQ0IBYLIZIJMLFixfh7OyMO3fusI5ISKNasGABEhMTsWLFCkydOhWbNm3C3bt3sXXrVqxZswYuLi6sIxJCFICuri7CwsLw9ddfyxzft28f3N3d8eTJE0bJCCE14uPj4eTkBJFIhOvXr6Nv3764desWJBIJLC0tkZCQwDoi4SADAwN4eHjITXIODg5GWFgYCgsLGSVrGrRSihDSpK5cuYK9e/fKHdfT06MbZqKQjh07hqioKNjb22PGjBmwtbWFkZERDAwMsGfPHipKEUIaRUVFBaytreWOW1lZobKykkEiQsjbli5dCm9vbwQGBkJDQwOHDx+Gnp4eXFxc4OjoyDoe4ajAwEDMnj0bSUlJtU5yVjRKrAMQQhSbtrY27t+/L3c8IyMDXbp0YZCIkKb17NkziEQiANX9o2ompHz22Wc4ffo0y2iEEAUydepUbNmyRe74tm3bqPhNFIJQKET79u0b9D+uys7OhqurKwCgVatWePnyJdTV1REUFFTr9ixCAGD69Ok4e/YsNDU1ERMTg5iYGGhqaiIlJQXTp09nHa/R0UopQkiTmjRpEnx9fXHw4EEIBAK8fv0aZ8+ehbe3t/SPNCGKRCQSoaCgAPr6+ujduzcOHDgAGxsbHDt2DNra2qzjEUIUSEREBE6dOoWBAwcCqH6TXlRUBFdXVyxatEj6OepbQ/ho/fr10p/fNY2Mq9q1ayftI9WpUyfk5+dLh/zQjgFSnwEDBmDPnj2sYzQL6ilFCGlS5eXlWLBgAXbu3Imqqiq0atUKVVVVmDx5Mnbu3AllZWXWEQlpVKGhoVBWVoaHhwfi4uIwZswYSCQSVFRUICQkBJ6enqwjEkIUgIODQ4M+JxAIqG8N4T2+TiMbN24cRo8eDTc3N3h7e+PIkSOYPn06YmJiIBQKERcXxzoi4biWMMmZilKEkGZRVFSEq1evoqSkBBYWFjA2NmYdiZBGV1FRAUdHR4SHh0u/44WFhUhLS4ORkRHMzc0ZJySEEEL4R11dHZmZmTAyMpI5npeXh/79+6OkpIRRsvrdvHkTJSUlMDc3R2lpKRYvXoxz587B2NgYISEhMDAwYB2RcFBLm+RM2/cIIc1CX18f+vr6rGMQ0qRUVFSQlZUlc8zAwIBuOgkhhJB/QUdHB0eOHJGbRnbkyBHo6OgwSvVuNT0mgeqtfOHh4QzTEL5YsmQJEhMTsWXLllonOSsaWilFCGlSEokEhw4dQmJiIh49eoTXr1/LnI+JiWGUjJCm4eXlhdatWyvkTQMhhPs2b96MJ0+eICAggHUUQhrNzp07MXv2bIwcObLWaWSK2PyZtFz6+vrSSc6amppIT0+HkZERoqOjsW/fPpw4cYJ1xEZFK6UIIU3q22+/xdatW+Hg4ICPPvoIAoGAdSRCmlRlZSUiIyMRFxcHKysrtGvXTuY8NRwmhDSlw4cPo6CggIpSRKFMnz4dJiYmCAsLk77QNDExQUpKirRIxRVCobDB97s1E3oJeVN9k5znzZvHMlqToKIUIaRJRUdHIyYmBqNGjWIdhZBmcfXqVVhaWgIAcnJyZM5RUZYQ0tTi4+NZRyCkSfBlGtmbUwMJ+RAtbZIzbd8jhDSp7t27448//kDv3r1ZRyGEEEIIITzHp2lkrq6usLe3h52dHXr06ME6DuGJljbJmYpShJAmtWvXLsTGxiIyMhJt27ZlHYcQQghRGDdu3MCGDRuQnZ0NoHo708KFC+lFEFE4fJ1G5ubmhuTkZOTn56Nz586ws7OTFqloEjWpTUuc5ExFKUJIk3r58iXGjx+Ps2fPwtDQECoqKjLn09PTGSUjhBBC+Ovw4cOYNGkSrK2t8cknnwAALly4gEuXLmH//v2YMGEC44SENJ4FCxYgMTERK1asqHUamYuLC+uI9bp79y5Onz6N5ORkJCcnIycnB506dcKdO3dYRyMcpKuri3PnzrWYwiX1lCKENKlp06YhLS0NU6ZMoUbnhBBCSCPx8fHB0qVLERQUJHN82bJl8PHxoaIUUSjHjh2TTiObMWMGbG1tYWRkBAMDA+zZs4fzRSmhUAgdHR0IhUJoa2ujVatW0NXVZR2LcNSUKVMQERHRYiY500opQkiTateuHU6ePInPPvuMdRRCCCFEYaipqSErKwtGRkYyx3Nzc9GvXz+UlZUxSkZI41NXV8e1a9egr6+Prl27IiYmBjY2NigoKICZmRlKSkpYR6zVd999h6SkJGRkZMDExES6fW/w4MEQCoWs4xGOcnd3R1RUFIyNjVvEJGdaKUUIaVLdunXjbPNJQgghhK/s7e1x5swZuaJUSkoKbG1tGaUipGnwdRrZmjVroKuri2XLlsHZ2Rk9e/ZkHYnwQEub5EwrpQghTer48ePYsGEDwsPDYWhoyDoOIYQQohDCw8MREBCAL7/8EgMHDgRQ3VPq4MGDCAwMROfOnaWfdXJyYhWTkEbB12lkYrEYycnJSEpKwpkzZ6CqqipdLWVvb09FKkJARSlCSBMTCoUoKytDZWUl1NTU5BqdP3v2jFEyQgghhL+UlJQa9DmBQMDZyWSENIQiTSMTi8UIDQ3Fnj178Pr1a/rdJAS0fY8Q0sTWr1/POgIhhBCicF6/fs06AiHNQkVFBVlZWTLHDAwMYGBgwChRw0kkEmRkZCApKQlJSUlISUlBcXExzM3NYWdnxzoeIZxAK6UIIYQQQgghhHCWl5cXWrduzbtpZEKhECUlJejXr590256trS2n+2AR0txopRQhpNm8evUK5eXlMseoCTohhBDyYUpLS5GcnIyioiK5v68eHh6MUhHS+CorKxEZGYm4uDheTSPbvXs3bG1t6X6XkHrQSilCSJMqLS2Fr68vDhw4gKdPn8qdp730hBBCyPvLyMjAqFGjUFZWhtLSUrRv3x5PnjyBmpoa9PT0cPPmTdYRCWk0Dg4OdZ4TCARISEhoxjSEkMZEK6UIIU3Kx8cHiYmJ2LJlC6ZOnYpNmzbh7t272Lp1K++WYBNCCCFc4eXlhTFjxiA8PBxaWlq4cOECVFRUMGXKFM5OIiPkQyUmJrKOQAhpIrRSihDSpPT19REVFQV7e3toamoiPT0dRkZGiI6Oxr59+3DixAnWEQkhhBDe0dbWRmpqKnr16gVtbW2cP38eJiYmSE1NxbRp03D9+nXWEQkhhJB3atgsWUII+UDPnj2DSCQCUN0/6tmzZwCAzz77DKdPn2YZjRBCCOEtFRUVKClV38rr6emhqKgIAKClpYXbt2+zjEYIIYQ0GBWlCCFNSiQSoaCgAADQu3dvHDhwAABw7NgxmjxCCCGEfCALCwtcunQJAGBnZ4eAgADs2bMH3377Lfr27cs4HSGEENIwtH2PENKkQkNDoaysDA8PD8TFxWHMmDGQSCSoqKhASEgI9b0ghBBCPsDly5fx999/w8HBAY8ePYKrqyvOnTsHY2NjREREoH///qwjEkIIIe9ERSlCSLMqLCxEWloajIyMYG5uzjoOIYQQQgghhBBGaPseIaTJVFRUYOjQocjNzZUeMzAwgLOzMxWkCCGEkH9hyJAhePHihdzx4uJiDBkypPkDEUIIIR+AilKEkCajoqKCrKws1jEIIYQQhZOUlITy8nK5469evcKZM2cYJCKEEELeXyvWAQghim3KlCmIiIjAmjVrWEchhBBCeO/Nlz3Xrl3DgwcPpP9dVVWF2NhYdOnShUU0Qggh5L1RUYoQ0qQqKysRGRmJuLg4WFlZoV27djLnQ0JCGCUjhBBC+Kd///4QCAQQCAS1btNr27YtNmzYwCAZIYQQ8v6o0TkhpNFlZWWhb9++UFJSgoODQ52fEwgESEhIaMZkhBBCCL8VFhZCIpFAJBLh4sWL0NXVlZ5TVVWFnp4elJWVGSYkhBBCGo6KUoSQRqesrIz79+9DT08PIpEIly5dgo6ODutYhBBCCCGEEEI4hLbvEUIanba2NgoKCqCnp4dbt27h9evXrCMRQgghCic/Px/r169HdnY2AMDU1BSenp7o0aMH42SEEEJIw1BRihDS6CZMmAA7Ozt06tQJAoEA1tbWdW4luHnzZjOnI4QQQvjv5MmTcHJyQv/+/TFo0CAAwNmzZ9GnTx8cO3YMw4YNY5yQEEIIeTfavkcIaRKxsbHIy8uDh4cHgoKCoKGhUevnPD09mzkZIYQQwn8WFhYYMWKE3HRbPz8/nDp1Cunp6YySEUIIIQ1HRSlCSJOaMWMGwsLC6ixKEUIIIeT9tWnTBleuXIGxsbHM8ZycHJibm+PVq1eMkhFCCCENp8Q6ACFEse3YsYMKUoQQQkgj09XVRWZmptzxzMxM6OnpNX8gQggh5ANQTylCCCGEEEJ4xs3NDd988w1u3ryJTz/9FEB1T6m1a9di0aJFjNMRQgghDUPb9wghhBBCCOEZiUSC9evXIzg4GPfu3QMAdO7cGUuWLIGHhwcEAgHjhIQQQsi7UVGKEEIIIYQQHvv7778BgLbLE0II4R0qShFCCCGEEEIIIYSQZkc9pQghhBBCCOEBS0tLxMfHQygUwsLCot4teunp6c2YjBBCCPkwVJQihBBCCCGEB8aOHYvWrVsDAMaNG8c2DCGEENIIaPseIYQQQgghhBBCCGl2SqwDEEIIIYQQQgghhJCWh7bvEUIIIYQQwgNCobDePlJvevbsWROnIYQQQv49KkoRQgghhBDCA+vXr5f+/PTpU6xcuRIjRozAJ598AgA4f/48Tp48CX9/f0YJCSGEkPdDPaUIIYQQQgjhmQkTJsDBwQELFy6UOb5x40bExcXht99+YxOMEEIIeQ9UlCKEEEIIIYRn1NXVkZmZCSMjI5njeXl56N+/P0pKShglI4QQQhqOGp0TQgghhBDCMzo6Ojhy5Ijc8SNHjkBHR4dBIkIIIeT9UU8pQgghhBBCeCYwMBCzZ89GUlISBgwYAABITU1FbGwstm/fzjgdIYQQ0jC0fY8QQgghhBAeSk1NRVhYGLKzswEAJiYm8PDwkBapCCGEEK6johQhhBBCCCGEEEIIaXa0fY8QQgghhBAee/XqFcrLy2WOaWpqMkpDCCGENBw1OieEEEIIIYRnysrKsHDhQujp6aFdu3YQCoUy/yOEEEL4gIpShBBCCCGE8MySJUuQkJCALVu2oHXr1vj5558RGBiIzp07IyoqinU8QgghpEGopxQhhBBCCCE8o6+vj6ioKNjb20NTUxPp6ekwMjJCdHQ09u3bhxMnTrCOSAghhLwTrZQihBBCCCGEZ549ewaRSASgun/Us2fPAACfffYZTp8+zTIaIYQQ0mBUlCKEEEIIIYRnRCIRCgoKAAC9e/fGgQMHAADHjh2DtrY2w2SEEEJIw9H2PUIIIYQQQngmNDQUysrK8PDwQFxcHMaMGQOJRIKKigqEhITA09OTdURCCCHknagoRQghhBBCCI9UVFTA0dER4eHhMDY2BgAUFhYiLS0NRkZGMDc3Z5yQEEIIaZhWrAMQQgghhBBCGk5FRQVZWVkyxwwMDGBgYMAoESGEEPJhqKcUIYQQQgghPDNlyhRERESwjkEIIYT8K7RSihBCCCGEEJ6prKxEZGQk4uLiYGVlhXbt2smcDwkJYZSMEEIIaTgqShFCCCGEEMIzV69ehaWlJQAgJydH5pxAIGARiRBCCHlv1OicEEIIIYQQQgghhDQ76ilFCCGEEEIIIYQQQpodFaUIIYQQQgghhBBCSLOjohQhhBBCCCGEEEIIaXZUlCKEEEIIIYQQQgghzY6KUoQQQgghhBBCCCGk2VFRihBCCCGEEEIIIYQ0OypKEUIIIYQQQgghhJBmR0UpQgghhBBCCCGEENLs/h+gLKiwzgpg3gAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Get feature importances\n",
        "importances = classifier.feature_importances_\n",
        "indices = np.argsort(importances)[::-1]\n",
        "\n",
        "# Plot feature importances\n",
        "plt.figure(figsize=(12, 8))\n",
        "plt.title(\"Feature Importances\")\n",
        "plt.bar(range(X_train.shape[1]), importances[indices], align=\"center\")\n",
        "plt.xticks(range(X_train.shape[1]), X_train.columns[indices], rotation=90)\n",
        "plt.xlim([-1, X_train.shape[1]])\n",
        "plt.tight_layout()\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 80,
      "metadata": {
        "id": "m7P33IpaiMwm",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2329e4d7-4ef4-4320-ae04-8421ba530fd9"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                           Feature  Importance Percentage\n",
            "0             wlan.fc.type_subtype           2.305916e+01\n",
            "1                     wlan.fc.type           2.014168e+01\n",
            "2                radiotap.datarate           1.480787e+01\n",
            "3                    frame.cap_len           9.680298e+00\n",
            "4       frame.time_delta_displayed           8.754823e+00\n",
            "5                 frame.time_delta           6.068344e+00\n",
            "6                        frame.len           5.425729e+00\n",
            "7                         wlan.seq           2.522028e+00\n",
            "8           radiotap.dbm_antsignal           2.346510e+00\n",
            "9                       wlan.fc.ds           2.317850e+00\n",
            "10                 wlan.fc.subtype           1.538551e+00\n",
            "11           radiotap.present.rate           1.531797e+00\n",
            "12                        data.len           7.979083e-01\n",
            "13                   wlan.duration           6.967801e-01\n",
            "14  radiotap.present.dbm_antsignal           2.439669e-01\n",
            "15             frame.time_relative           6.365068e-02\n",
            "16                       wlan.frag           1.538452e-03\n",
            "17            radiotap.present.ext           1.517194e-03\n",
            "18        radiotap.present.antenna           4.149340e-08\n",
            "19       wlan.ba.control.ackpolicy           0.000000e+00\n",
            "20                    wlan.qos.tid           0.000000e+00\n",
            "21               wlan.qos.priority           0.000000e+00\n",
            "22        radiotap.present.rtap_ns           0.000000e+00\n"
          ]
        }
      ],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "# Get feature importances\n",
        "importances = classifier.feature_importances_\n",
        "indices = np.argsort(importances)[::-1]\n",
        "\n",
        "# Calculate the percentage of each feature's importance\n",
        "importance_percentages = (importances / importances.sum()) * 100\n",
        "\n",
        "# Create a DataFrame to hold the feature names and their importances\n",
        "feature_importance_df = pd.DataFrame({\n",
        "    'Feature': X_train.columns[indices],\n",
        "    'Importance Percentage': importance_percentages[indices]\n",
        "})\n",
        "\n",
        "# Display the feature importances with percentages\n",
        "print(feature_importance_df)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "18fmj4X7bimF"
      },
      "source": [
        "## 2.3 KNN , Naive Bayes Model and Feather Preprocessing"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dgZnhtHNcPnH"
      },
      "source": [
        "### 2.3.1 Preprocessing"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 104,
      "metadata": {
        "id": "njDIs2aPcUAA",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e42c8efb-977f-4a4a-e831-6a73c604bcc8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/drive/MyDrive/Dataset AWID/Zips/AWID2/AWID2_Dataset\n"
          ]
        }
      ],
      "source": [
        "cd /content/drive/MyDrive/Dataset AWID/Zips/AWID2/AWID2_Dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 105,
      "metadata": {
        "id": "3W7meoqeiiah",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 290
        },
        "outputId": "f93b2f88-4401-48be-c96c-97d84b2bdba8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-105-48cfa3d9674b>:3: DtypeWarning: Columns (11,19,20,29) have mixed types. Specify dtype option on import or set low_memory=False.\n",
            "  df = pd.read_csv('Evil_Twin-Dataset_Updated.csv')\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   frame.time_epoch  frame.time_delta  frame.time_delta_displayed  \\\n",
              "0      1.425262e+09          0.000000                    0.000000   \n",
              "1      1.425262e+09          0.000007                    0.000007   \n",
              "2      1.425262e+09          0.000007                    0.000007   \n",
              "3      1.425262e+09          0.000768                    0.000768   \n",
              "4      1.425262e+09          0.000023                    0.000023   \n",
              "\n",
              "   frame.time_relative  frame.len  frame.cap_len  radiotap.length  \\\n",
              "0             0.000000         52             52               38   \n",
              "1             0.000007        110            110               38   \n",
              "2             0.000014         52             52               38   \n",
              "3             0.000782        110            110               38   \n",
              "4             0.002247         52             52               38   \n",
              "\n",
              "   radiotap.present.rate  radiotap.present.dbm_antsignal  \\\n",
              "0                      1                               1   \n",
              "1                      1                               1   \n",
              "2                      1                               1   \n",
              "3                      1                               1   \n",
              "4                      1                               1   \n",
              "\n",
              "   radiotap.present.antenna  ...  wlan.qos.tid wlan.qos.priority  \\\n",
              "0                         0  ...             ?                 ?   \n",
              "1                         0  ...             ?                 ?   \n",
              "2                         0  ...             ?                 ?   \n",
              "3                         0  ...             ?                 ?   \n",
              "4                         0  ...             ?                 ?   \n",
              "\n",
              "   wlan.qos.eosp  wlan.qos.ack  wlan.qos.amsdupresent  wlan.qos.bit4  \\\n",
              "0              ?             ?                      ?              ?   \n",
              "1              ?             ?                      ?              ?   \n",
              "2              ?             ?                      ?              ?   \n",
              "3              ?             ?                      ?              ?   \n",
              "4              ?             ?                      ?              ?   \n",
              "\n",
              "   wlan.qos.txop_dur_req  wlan.qos.buf_state_indicated.1  data.len   class  \n",
              "0                      ?                               ?         ?  normal  \n",
              "1                      ?                               ?        36  normal  \n",
              "2                      ?                               ?         ?  normal  \n",
              "3                      ?                               ?        36  normal  \n",
              "4                      ?                               ?         ?  normal  \n",
              "\n",
              "[5 rows x 104 columns]"
            ],
            "text/html": [
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              "  <div id=\"df-9e11aa0d-8d77-4fda-9926-fe7d899d450a\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
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              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>frame.time_epoch</th>\n",
              "      <th>frame.time_delta</th>\n",
              "      <th>frame.time_delta_displayed</th>\n",
              "      <th>frame.time_relative</th>\n",
              "      <th>frame.len</th>\n",
              "      <th>frame.cap_len</th>\n",
              "      <th>radiotap.length</th>\n",
              "      <th>radiotap.present.rate</th>\n",
              "      <th>radiotap.present.dbm_antsignal</th>\n",
              "      <th>radiotap.present.antenna</th>\n",
              "      <th>...</th>\n",
              "      <th>wlan.qos.tid</th>\n",
              "      <th>wlan.qos.priority</th>\n",
              "      <th>wlan.qos.eosp</th>\n",
              "      <th>wlan.qos.ack</th>\n",
              "      <th>wlan.qos.amsdupresent</th>\n",
              "      <th>wlan.qos.bit4</th>\n",
              "      <th>wlan.qos.txop_dur_req</th>\n",
              "      <th>wlan.qos.buf_state_indicated.1</th>\n",
              "      <th>data.len</th>\n",
              "      <th>class</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1.425262e+09</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>52</td>\n",
              "      <td>52</td>\n",
              "      <td>38</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1.425262e+09</td>\n",
              "      <td>0.000007</td>\n",
              "      <td>0.000007</td>\n",
              "      <td>0.000007</td>\n",
              "      <td>110</td>\n",
              "      <td>110</td>\n",
              "      <td>38</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>36</td>\n",
              "      <td>normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1.425262e+09</td>\n",
              "      <td>0.000007</td>\n",
              "      <td>0.000007</td>\n",
              "      <td>0.000014</td>\n",
              "      <td>52</td>\n",
              "      <td>52</td>\n",
              "      <td>38</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1.425262e+09</td>\n",
              "      <td>0.000768</td>\n",
              "      <td>0.000768</td>\n",
              "      <td>0.000782</td>\n",
              "      <td>110</td>\n",
              "      <td>110</td>\n",
              "      <td>38</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>36</td>\n",
              "      <td>normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1.425262e+09</td>\n",
              "      <td>0.000023</td>\n",
              "      <td>0.000023</td>\n",
              "      <td>0.002247</td>\n",
              "      <td>52</td>\n",
              "      <td>52</td>\n",
              "      <td>38</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>?</td>\n",
              "      <td>normal</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 104 columns</p>\n",
              "</div>\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-9e11aa0d-8d77-4fda-9926-fe7d899d450a button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-9e11aa0d-8d77-4fda-9926-fe7d899d450a');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "\n",
              "<style>\n",
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              "      --disabled-fill-color: #AAA;\n",
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              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
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              "\n",
              "  .colab-df-quickchart {\n",
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              "    border: none;\n",
              "    border-radius: 50%;\n",
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              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
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              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
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              "      border-left-color: var(--fill-color);\n",
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              "    30% {\n",
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              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "    90% {\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-f03733fc-b9df-456b-a8ee-c974d0d37028 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df"
            }
          },
          "metadata": {},
          "execution_count": 105
        }
      ],
      "source": [
        "import pandas as pd\n",
        "# Load CSV file\n",
        "df = pd.read_csv('Evil_Twin-Dataset_Updated.csv')\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 106,
      "metadata": {
        "id": "tIXjEc6fimvJ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c0bb007e-b4cd-44c9-a059-3f85d0c22c17"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Class counts in the DataFrame: {'normal': 12998132, 'evil_twin': 50643}\n",
            "normal: 12998132\n",
            "evil_twin: 50643\n"
          ]
        }
      ],
      "source": [
        "def unique_class_counts(df, target_column):\n",
        "    class_counts = df[target_column].value_counts().to_dict()\n",
        "    return class_counts\n",
        "\n",
        "# Get unique class counts\n",
        "class_counts = unique_class_counts(df, 'class')\n",
        "print(f\"Class counts in the DataFrame: {class_counts}\")\n",
        "for class_name, count in class_counts.items():\n",
        "    print(f\"{class_name}: {count}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 107,
      "metadata": {
        "id": "KSyH36b-i8hN"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "#Replace ? to NaN\n",
        "pre_num_cols = len(df.columns)\n",
        "df.replace('?', np.nan, inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 108,
      "metadata": {
        "id": "Tdj9D8aQi-XI",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c106e9b2-455c-42f9-d026-09f6a66d7fb5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of columns dropped: 50\n"
          ]
        }
      ],
      "source": [
        "import numpy as np\n",
        "#Remove Columns (Featuers) which has more than 90% missing values\n",
        "pre_num_cols = len(df.columns)\n",
        "df.replace('?', np.nan, inplace=True)\n",
        "df.dropna(axis='columns', thresh=len(df.index)*0.10, inplace=True)\n",
        "\n",
        "\n",
        "post_num_cols = len(df.columns)\n",
        "print(f\"Number of columns dropped: {pre_num_cols - post_num_cols}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 109,
      "metadata": {
        "id": "zf2jVhfAjEZd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        },
        "outputId": "0a9db233-d59e-4878-b523-81a10234378c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   frame.time_epoch  frame.time_delta  frame.time_delta_displayed  \\\n",
              "0      1.425262e+09          0.000000                    0.000000   \n",
              "1      1.425262e+09          0.000007                    0.000007   \n",
              "2      1.425262e+09          0.000007                    0.000007   \n",
              "3      1.425262e+09          0.000768                    0.000768   \n",
              "4      1.425262e+09          0.000023                    0.000023   \n",
              "\n",
              "   frame.time_relative  frame.len  frame.cap_len  radiotap.length  \\\n",
              "0             0.000000         52             52               38   \n",
              "1             0.000007        110            110               38   \n",
              "2             0.000014         52             52               38   \n",
              "3             0.000782        110            110               38   \n",
              "4             0.002247         52             52               38   \n",
              "\n",
              "   radiotap.present.rate  radiotap.present.dbm_antsignal  \\\n",
              "0                      1                               1   \n",
              "1                      1                               1   \n",
              "2                      1                               1   \n",
              "3                      1                               1   \n",
              "4                      1                               1   \n",
              "\n",
              "   radiotap.present.antenna  ...  wlan.wep.key wlan.wep.icv  wlan.qos.tid  \\\n",
              "0                         0  ...           NaN          NaN           NaN   \n",
              "1                         0  ...             0   0xbf54e133           NaN   \n",
              "2                         0  ...           NaN          NaN           NaN   \n",
              "3                         0  ...             0   0xe1688921           NaN   \n",
              "4                         0  ...           NaN          NaN           NaN   \n",
              "\n",
              "   wlan.qos.priority  wlan.qos.ack  wlan.qos.amsdupresent  wlan.qos.bit4  \\\n",
              "0                NaN           NaN                    NaN            NaN   \n",
              "1                NaN           NaN                    NaN            NaN   \n",
              "2                NaN           NaN                    NaN            NaN   \n",
              "3                NaN           NaN                    NaN            NaN   \n",
              "4                NaN           NaN                    NaN            NaN   \n",
              "\n",
              "   wlan.qos.txop_dur_req  data.len   class  \n",
              "0                    NaN       NaN  normal  \n",
              "1                    NaN        36  normal  \n",
              "2                    NaN       NaN  normal  \n",
              "3                    NaN        36  normal  \n",
              "4                    NaN       NaN  normal  \n",
              "\n",
              "[5 rows x 54 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-72b964b6-9f64-46ab-98fe-bb1630ce7ee6\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "    .dataframe tbody tr th {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>frame.time_epoch</th>\n",
              "      <th>frame.time_delta</th>\n",
              "      <th>frame.time_delta_displayed</th>\n",
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              "      <th>frame.len</th>\n",
              "      <th>frame.cap_len</th>\n",
              "      <th>radiotap.length</th>\n",
              "      <th>radiotap.present.rate</th>\n",
              "      <th>radiotap.present.dbm_antsignal</th>\n",
              "      <th>radiotap.present.antenna</th>\n",
              "      <th>...</th>\n",
              "      <th>wlan.wep.key</th>\n",
              "      <th>wlan.wep.icv</th>\n",
              "      <th>wlan.qos.tid</th>\n",
              "      <th>wlan.qos.priority</th>\n",
              "      <th>wlan.qos.ack</th>\n",
              "      <th>wlan.qos.amsdupresent</th>\n",
              "      <th>wlan.qos.bit4</th>\n",
              "      <th>wlan.qos.txop_dur_req</th>\n",
              "      <th>data.len</th>\n",
              "      <th>class</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1.425262e+09</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>52</td>\n",
              "      <td>52</td>\n",
              "      <td>38</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1.425262e+09</td>\n",
              "      <td>0.000007</td>\n",
              "      <td>0.000007</td>\n",
              "      <td>0.000007</td>\n",
              "      <td>110</td>\n",
              "      <td>110</td>\n",
              "      <td>38</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0xbf54e133</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>36</td>\n",
              "      <td>normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1.425262e+09</td>\n",
              "      <td>0.000007</td>\n",
              "      <td>0.000007</td>\n",
              "      <td>0.000014</td>\n",
              "      <td>52</td>\n",
              "      <td>52</td>\n",
              "      <td>38</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1.425262e+09</td>\n",
              "      <td>0.000768</td>\n",
              "      <td>0.000768</td>\n",
              "      <td>0.000782</td>\n",
              "      <td>110</td>\n",
              "      <td>110</td>\n",
              "      <td>38</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0xe1688921</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>36</td>\n",
              "      <td>normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1.425262e+09</td>\n",
              "      <td>0.000023</td>\n",
              "      <td>0.000023</td>\n",
              "      <td>0.002247</td>\n",
              "      <td>52</td>\n",
              "      <td>52</td>\n",
              "      <td>38</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>normal</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 54 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "    .colab-df-buttons div {\n",
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              "    [theme=dark] .colab-df-convert:hover {\n",
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              "        document.querySelector('#df-72b964b6-9f64-46ab-98fe-bb1630ce7ee6 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-72b964b6-9f64-46ab-98fe-bb1630ce7ee6');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "        docLink.innerHTML = docLinkHtml;\n",
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              "    </g>\n",
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              "  </button>\n",
              "\n",
              "<style>\n",
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              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
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              "      --disabled-bg-color: #3B4455;\n",
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              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
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              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
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              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
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              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
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              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
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              "      border-bottom-color: var(--fill-color);\n",
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              "    20% {\n",
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              "      border-right-color: var(--fill-color);\n",
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              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "    (() => {\n",
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              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df"
            }
          },
          "metadata": {},
          "execution_count": 109
        }
      ],
      "source": [
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 110,
      "metadata": {
        "id": "j59tT1I0jIHv"
      },
      "outputs": [],
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "import numpy as np\n",
        "# Encording the Featuers\n",
        "encoder1= LabelEncoder().fit(df['wlan.fc.type'])\n",
        "df['wlan.fc.type'] = encoder1.transform(df['wlan.fc.type'])\n",
        "\n",
        "encoder2= LabelEncoder().fit(df['wlan.fc.type_subtype'])\n",
        "df['wlan.fc.type_subtype'] = encoder2.transform(df['wlan.fc.type_subtype'])\n",
        "\n",
        "encoder3= LabelEncoder().fit(df['wlan.fc.ds'])\n",
        "df['wlan.fc.ds'] = encoder3.transform(df['wlan.fc.ds'])\n",
        "\n",
        "encoder4= LabelEncoder().fit(df['wlan.sa'])\n",
        "df['wlan.sa'] = encoder4.transform(df['wlan.sa'])\n",
        "\n",
        "encoder5= LabelEncoder().fit(df['wlan.ta'])\n",
        "df['wlan.ta'] = encoder5.transform(df['wlan.ta'])\n",
        "\n",
        "encoder6= LabelEncoder().fit(df['wlan.da'])\n",
        "df['wlan.da'] = encoder6.transform(df['wlan.da'])\n",
        "\n",
        "encoder7= LabelEncoder().fit(df['wlan.ra'])\n",
        "df['wlan.ra'] = encoder7.transform(df['wlan.ra'])\n",
        "\n",
        "encoder8= LabelEncoder().fit(df['wlan.bssid'])\n",
        "df['wlan.bssid'] = encoder8.transform(df['wlan.bssid'])\n",
        "\n",
        "encoder9= LabelEncoder().fit(df['wlan.wep.iv'])\n",
        "df['wlan.wep.iv'] = encoder9.transform(df['wlan.wep.iv'])\n",
        "\n",
        "encoder10= LabelEncoder().fit(df['wlan.wep.icv'])\n",
        "df['wlan.wep.icv'] = encoder10.transform(df['wlan.wep.icv'])\n",
        "\n",
        "encoder11= LabelEncoder().fit(df['wlan.qos.ack'])\n",
        "df['wlan.qos.ack'] = encoder11.transform(df['wlan.qos.ack'])\n",
        "\n",
        "encoder12= LabelEncoder().fit(df['wlan.ba.bm'])\n",
        "df['wlan.ba.bm'] = encoder12.transform(df['wlan.ba.bm'])\n",
        "\n",
        "# encoder13 = LabelEncoder().fit(df['wlan.bar.type'])\n",
        "# df['wlan.bar.type'] = encoder13.transform(df['wlan.bar.type'])\n",
        "\n",
        "# encoder14 = LabelEncoder().fit(df['wlan.bar.compressed.tidinfo'])\n",
        "# df['wlan.bar.compressed.tidinfo'] = encoder14.transform(df['wlan.bar.compressed.tidinfo'])\n",
        "\n",
        "# encoder15 = LabelEncoder().fit(df['wlan_mgt.fixed.capabilities.cfpoll.ap'])\n",
        "# df['wlan_mgt.fixed.capabilities.cfpoll.ap'] = encoder15.transform(df['wlan_mgt.fixed.capabilities.cfpoll.ap'])\n",
        "\n",
        "# encoder16 = LabelEncoder().fit(df['wlan_mgt.fixed.listen_ival'])\n",
        "# df['wlan_mgt.fixed.listen_ival'] = encoder16.transform(df['wlan_mgt.fixed.listen_ival'])\n",
        "\n",
        "# encoder17 = LabelEncoder().fit(df['wlan_mgt.fixed.current_ap'])\n",
        "# df['wlan_mgt.fixed.current_ap'] = encoder17.transform(df['wlan_mgt.fixed.current_ap'])\n",
        "\n",
        "# encoder18 = LabelEncoder().fit(df['wlan_mgt.fixed.status_code'])\n",
        "# df['wlan_mgt.fixed.status_code'] = encoder18.transform(df['wlan_mgt.fixed.status_code'])\n",
        "\n",
        "# encoder19 = LabelEncoder().fit(df['wlan_mgt.fixed.timestamp'])\n",
        "# df['wlan_mgt.fixed.timestamp'] = encoder19.transform(df['wlan_mgt.fixed.timestamp'])\n",
        "\n",
        "# encoder20 = LabelEncoder().fit(df['wlan_mgt.fixed.aid'])\n",
        "# df['wlan_mgt.fixed.aid'] = encoder20.transform(df['wlan_mgt.fixed.aid'])\n",
        "\n",
        "# encoder21 = LabelEncoder().fit(df['wlan_mgt.fixed.reason_code'])\n",
        "# df['wlan_mgt.fixed.reason_code'] = encoder21.transform(df['wlan_mgt.fixed.reason_code'])\n",
        "\n",
        "# encoder22 = LabelEncoder().fit(df['wlan_mgt.fixed.auth_seq'])\n",
        "# df['wlan_mgt.fixed.auth_seq'] = encoder22.transform(df['wlan_mgt.fixed.auth_seq'])\n",
        "\n",
        "# encoder23 = LabelEncoder().fit(df['wlan_mgt.ssid'])\n",
        "# df['wlan_mgt.ssid'] = encoder23.transform(df['wlan_mgt.ssid'])\n",
        "\n",
        "# encoder24 = LabelEncoder().fit(df['wlan_mgt.tim.bmapctl.offset'])\n",
        "# df['wlan_mgt.tim.bmapctl.offset'] = encoder24.transform(df['wlan_mgt.tim.bmapctl.offset'])\n",
        "\n",
        "# encoder25 = LabelEncoder().fit(df['wlan_mgt.country_info.environment'])\n",
        "# df['wlan_mgt.country_info.environment'] = encoder25.transform(df['wlan_mgt.country_info.environment'])\n",
        "\n",
        "# encoder26 = LabelEncoder().fit(df['wlan_mgt.rsn.capabilities.ptksa_replay'])\n",
        "# df['wlan_mgt.rsn.capabilities.ptksa_replay'] = encoder26.transform(df['wlan_mgt.rsn.capabilities.ptksa_replay'])\n",
        "\n",
        "# encoder27 = LabelEncoder().fit(df['wlan_mgt.rsn.capabilities.gtksa_replay_counter'])\n",
        "# df['wlan_mgt.rsn.capabilities.gtksa_replay_counter'] = encoder27.transform(df['wlan_mgt.rsn.capabilities.gtksa_replay_counter'])\n",
        "\n",
        "# encoder28 = LabelEncoder().fit(df['wlan.tkip.extiv'])\n",
        "# df['wlan.tkip.extiv'] = encoder28.transform(df['wlan.tkip.extiv'])\n",
        "\n",
        "# encoder29 = LabelEncoder().fit(df['wlan.ccmp.extiv'])\n",
        "# df['wlan.ccmp.extiv'] = encoder29.transform(df['wlan.ccmp.extiv'])\n",
        "\n",
        "df.replace(np.nan,0, inplace=True) # Replace NAN to 0\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 111,
      "metadata": {
        "id": "HG29zEJHjMlx",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "751bf5cf-ba2e-49e4-e4a4-050c6215a56f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "54\n",
            "['frame.time_epoch', 'frame.time_delta', 'frame.time_delta_displayed', 'frame.time_relative', 'frame.len', 'frame.cap_len', 'radiotap.length', 'radiotap.present.rate', 'radiotap.present.dbm_antsignal', 'radiotap.present.antenna', 'radiotap.present.mcs', 'radiotap.present.ampdu', 'radiotap.present.rtap_ns', 'radiotap.present.ext', 'radiotap.mactime', 'radiotap.flags.preamble', 'radiotap.datarate', 'radiotap.channel.type.ofdm', 'radiotap.channel.type.dynamic', 'radiotap.dbm_antsignal', 'radiotap.antenna', 'wlan.fc.type_subtype', 'wlan.fc.type', 'wlan.fc.subtype', 'wlan.fc.ds', 'wlan.fc.retry', 'wlan.fc.pwrmgt', 'wlan.fc.moredata', 'wlan.fc.protected', 'wlan.duration', 'wlan.ra', 'wlan.da', 'wlan.ta', 'wlan.sa', 'wlan.bssid', 'wlan.frag', 'wlan.seq', 'wlan.ba.control.ackpolicy', 'wlan.ba.control.multitid', 'wlan.ba.control.cbitmap', 'wlan.ba.bm', 'wlan_mgt.fixed.fragment', 'wlan_mgt.fixed.sequence', 'wlan.wep.iv', 'wlan.wep.key', 'wlan.wep.icv', 'wlan.qos.tid', 'wlan.qos.priority', 'wlan.qos.ack', 'wlan.qos.amsdupresent', 'wlan.qos.bit4', 'wlan.qos.txop_dur_req', 'data.len', 'class']\n"
          ]
        }
      ],
      "source": [
        "column_names_list = df.columns.tolist() # Column count and name of the columns\n",
        "print(len(column_names_list))\n",
        "print(column_names_list)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 112,
      "metadata": {
        "id": "fDi9SKNrjQPJ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d46dd729-2822-4f70-fbe8-443957dce5c0"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Class counts in the DataFrame: {'normal': 12998132, 'evil_twin': 50643}\n",
            "normal: 12998132\n",
            "evil_twin: 50643\n"
          ]
        }
      ],
      "source": [
        "def unique_class_counts(df, target_column):\n",
        "    class_counts = df[target_column].value_counts().to_dict()\n",
        "    return class_counts\n",
        "\n",
        "# Get unique class counts\n",
        "class_counts = unique_class_counts(df, 'class')\n",
        "print(f\"Class counts in the DataFrame: {class_counts}\")\n",
        "for class_name, count in class_counts.items():\n",
        "    print(f\"{class_name}: {count}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 113,
      "metadata": {
        "id": "zbtn2-m_jR_g"
      },
      "outputs": [],
      "source": [
        "# Filter rows where the 'class' column is equal to 'normal' (extracting normal class data)\n",
        "df1 = df.loc[df['class'] == 'normal']\n",
        "\n",
        "# Randomly sample 2% of the rows from the normal class data without replacement\n",
        "df2 = df1.sample(frac=0.02, replace=False, random_state=None, axis=0)\n",
        "\n",
        "# Filter rows where the 'class' column is NOT equal to 'normal' (all other class data)\n",
        "df3 = df.loc[df['class'] != 'normal']\n",
        "\n",
        "# Combine the sampled 'normal' class rows (df2) with all rows from other classes (df3)\n",
        "df4 = pd.concat([df2, df3])\n",
        "\n",
        "# Update the original dataframe to include only the reduced normal class data and all other classes\n",
        "df = df4\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 114,
      "metadata": {
        "id": "V4UhBBRqjTuj",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3f3a1573-3e09-418e-b6f0-44cbb407e85a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Class counts in the DataFrame: {'normal': 259963, 'evil_twin': 50643}\n",
            "normal: 259963\n",
            "evil_twin: 50643\n"
          ]
        }
      ],
      "source": [
        "def unique_class_counts(df, target_column):\n",
        "    class_counts = df[target_column].value_counts().to_dict()\n",
        "    return class_counts\n",
        "\n",
        "# Get unique class counts\n",
        "class_counts = unique_class_counts(df, 'class')\n",
        "print(f\"Class counts in the DataFrame: {class_counts}\")\n",
        "for class_name, count in class_counts.items():\n",
        "    print(f\"{class_name}: {count}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 118,
      "metadata": {
        "id": "08KbNuSBjW-9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        },
        "outputId": "c0109f64-b37d-4874-8ee4-0087cbaf3ea2"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          frame.time_epoch  frame.time_delta  frame.time_delta_displayed  \\\n",
              "1815433       1.425264e+09          0.000022                    0.000022   \n",
              "7004041       1.425269e+09          0.000011                    0.000011   \n",
              "7930412       1.425096e+09          0.001996                    0.001996   \n",
              "2186283       1.425264e+09          0.000009                    0.000009   \n",
              "10628767      1.425099e+09          0.000004                    0.000004   \n",
              "\n",
              "          frame.time_relative  frame.len  frame.cap_len  radiotap.length  \\\n",
              "1815433            147.415610         52             52               38   \n",
              "7004041            417.834493        130            130               40   \n",
              "7930412             31.526571       1578           1578               40   \n",
              "2186283            359.462944       1590           1590               52   \n",
              "10628767             7.028738       1584           1584               38   \n",
              "\n",
              "          radiotap.present.rate  radiotap.present.dbm_antsignal  \\\n",
              "1815433                       1                               1   \n",
              "7004041                       0                               0   \n",
              "7930412                       0                               0   \n",
              "2186283                       0                               1   \n",
              "10628767                      1                               1   \n",
              "\n",
              "          radiotap.present.antenna  ...  wlan.wep.key wlan.wep.icv  \\\n",
              "1815433                          0  ...             0      1962903   \n",
              "7004041                          1  ...             0      1962903   \n",
              "7930412                          1  ...             0      1962903   \n",
              "2186283                          0  ...             0      1962903   \n",
              "10628767                         0  ...             0      1880491   \n",
              "\n",
              "          wlan.qos.tid  wlan.qos.priority  wlan.qos.ack  \\\n",
              "1815433              0                  0             4   \n",
              "7004041              0                  0             1   \n",
              "7930412              0                  0             0   \n",
              "2186283              0                  0             1   \n",
              "10628767             0                  0             0   \n",
              "\n",
              "          wlan.qos.amsdupresent  wlan.qos.bit4  wlan.qos.txop_dur_req  \\\n",
              "1815433                       0              0                      0   \n",
              "7004041                       0              0                      0   \n",
              "7930412                       0              0                      0   \n",
              "2186283                       0              0                      0   \n",
              "10628767                      0              0                      0   \n",
              "\n",
              "          data.len   class  \n",
              "1815433          0  normal  \n",
              "7004041          0  normal  \n",
              "7930412       1448  normal  \n",
              "2186283       1448  normal  \n",
              "10628767      1508  normal  \n",
              "\n",
              "[5 rows x 54 columns]"
            ],
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              "      <th>frame.time_epoch</th>\n",
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              "      <td>1.425096e+09</td>\n",
              "      <td>0.001996</td>\n",
              "      <td>0.001996</td>\n",
              "      <td>31.526571</td>\n",
              "      <td>1578</td>\n",
              "      <td>1578</td>\n",
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              "      <td>7.028738</td>\n",
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              "<p>5 rows × 54 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
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              "\n",
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              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-e27f6d60-ccda-4711-b23c-5b17e33959b2');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
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              "\n",
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              "\n",
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              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
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              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
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              "      border-color: transparent;\n",
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              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-506401f1-a0ca-4d48-bdc5-5a7e24c1cf8a button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df"
            }
          },
          "metadata": {},
          "execution_count": 118
        }
      ],
      "source": [
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_normalized = df"
      ],
      "metadata": {
        "id": "5G7cM1MtXw9k"
      },
      "execution_count": 119,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 120,
      "metadata": {
        "id": "PN4nnhZ6jiBi",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        },
        "outputId": "62a44e8b-c94b-49e4-da7c-37f9f3047ba3"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          frame.time_epoch  frame.time_delta  frame.time_delta_displayed  \\\n",
              "1815433       1.425264e+09          0.000022                    0.000022   \n",
              "7004041       1.425269e+09          0.000011                    0.000011   \n",
              "7930412       1.425096e+09          0.001996                    0.001996   \n",
              "2186283       1.425264e+09          0.000009                    0.000009   \n",
              "10628767      1.425099e+09          0.000004                    0.000004   \n",
              "\n",
              "          frame.time_relative  frame.len  frame.cap_len  radiotap.length  \\\n",
              "1815433            147.415610         52             52               38   \n",
              "7004041            417.834493        130            130               40   \n",
              "7930412             31.526571       1578           1578               40   \n",
              "2186283            359.462944       1590           1590               52   \n",
              "10628767             7.028738       1584           1584               38   \n",
              "\n",
              "          radiotap.present.rate  radiotap.present.dbm_antsignal  \\\n",
              "1815433                       1                               1   \n",
              "7004041                       0                               0   \n",
              "7930412                       0                               0   \n",
              "2186283                       0                               1   \n",
              "10628767                      1                               1   \n",
              "\n",
              "          radiotap.present.antenna  ...  wlan.wep.key wlan.wep.icv  \\\n",
              "1815433                          0  ...             0      1962903   \n",
              "7004041                          1  ...             0      1962903   \n",
              "7930412                          1  ...             0      1962903   \n",
              "2186283                          0  ...             0      1962903   \n",
              "10628767                         0  ...             0      1880491   \n",
              "\n",
              "          wlan.qos.tid  wlan.qos.priority  wlan.qos.ack  \\\n",
              "1815433              0                  0             4   \n",
              "7004041              0                  0             1   \n",
              "7930412              0                  0             0   \n",
              "2186283              0                  0             1   \n",
              "10628767             0                  0             0   \n",
              "\n",
              "          wlan.qos.amsdupresent  wlan.qos.bit4  wlan.qos.txop_dur_req  \\\n",
              "1815433                       0              0                      0   \n",
              "7004041                       0              0                      0   \n",
              "7930412                       0              0                      0   \n",
              "2186283                       0              0                      0   \n",
              "10628767                      0              0                      0   \n",
              "\n",
              "          data.len   class  \n",
              "1815433          0  normal  \n",
              "7004041          0  normal  \n",
              "7930412       1448  normal  \n",
              "2186283       1448  normal  \n",
              "10628767      1508  normal  \n",
              "\n",
              "[5 rows x 54 columns]"
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              "  <div id=\"df-bf608b01-b75e-49c7-89d2-d3ec34f75938\" class=\"colab-df-container\">\n",
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              "      <th>frame.time_epoch</th>\n",
              "      <th>frame.time_delta</th>\n",
              "      <th>frame.time_delta_displayed</th>\n",
              "      <th>frame.time_relative</th>\n",
              "      <th>frame.len</th>\n",
              "      <th>frame.cap_len</th>\n",
              "      <th>radiotap.length</th>\n",
              "      <th>radiotap.present.rate</th>\n",
              "      <th>radiotap.present.dbm_antsignal</th>\n",
              "      <th>radiotap.present.antenna</th>\n",
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              "      <th>wlan.qos.tid</th>\n",
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              "      <th>wlan.qos.bit4</th>\n",
              "      <th>wlan.qos.txop_dur_req</th>\n",
              "      <th>data.len</th>\n",
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              "      <th>1815433</th>\n",
              "      <td>1.425264e+09</td>\n",
              "      <td>0.000022</td>\n",
              "      <td>0.000022</td>\n",
              "      <td>147.415610</td>\n",
              "      <td>52</td>\n",
              "      <td>52</td>\n",
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              "      <td>1</td>\n",
              "      <td>1</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>7004041</th>\n",
              "      <td>1.425269e+09</td>\n",
              "      <td>0.000011</td>\n",
              "      <td>0.000011</td>\n",
              "      <td>417.834493</td>\n",
              "      <td>130</td>\n",
              "      <td>130</td>\n",
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              "      <td>0</td>\n",
              "      <td>normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7930412</th>\n",
              "      <td>1.425096e+09</td>\n",
              "      <td>0.001996</td>\n",
              "      <td>0.001996</td>\n",
              "      <td>31.526571</td>\n",
              "      <td>1578</td>\n",
              "      <td>1578</td>\n",
              "      <td>40</td>\n",
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              "    <tr>\n",
              "      <th>2186283</th>\n",
              "      <td>1.425264e+09</td>\n",
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              "      <td>359.462944</td>\n",
              "      <td>1590</td>\n",
              "      <td>1590</td>\n",
              "      <td>52</td>\n",
              "      <td>0</td>\n",
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              "      <td>normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10628767</th>\n",
              "      <td>1.425099e+09</td>\n",
              "      <td>0.000004</td>\n",
              "      <td>0.000004</td>\n",
              "      <td>7.028738</td>\n",
              "      <td>1584</td>\n",
              "      <td>1584</td>\n",
              "      <td>38</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1508</td>\n",
              "      <td>normal</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 54 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-bf608b01-b75e-49c7-89d2-d3ec34f75938')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-bf608b01-b75e-49c7-89d2-d3ec34f75938 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-bf608b01-b75e-49c7-89d2-d3ec34f75938');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-cf442c38-2356-4d19-bd62-20869373e210\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-cf442c38-2356-4d19-bd62-20869373e210')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-cf442c38-2356-4d19-bd62-20869373e210 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_normalized"
            }
          },
          "metadata": {},
          "execution_count": 120
        }
      ],
      "source": [
        "df_normalized.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 121,
      "metadata": {
        "id": "40Ffw-mdjkaZ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "95309eda-7df7-4a40-d0f5-34c375535c7a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Class counts in the DataFrame: {'normal': 259963, 'evil_twin': 50643}\n",
            "normal: 259963\n",
            "evil_twin: 50643\n"
          ]
        }
      ],
      "source": [
        "def unique_class_counts(df_normalized, target_column):\n",
        "    class_counts = df_normalized[target_column].value_counts().to_dict()\n",
        "    return class_counts\n",
        "\n",
        "# Get unique class counts\n",
        "class_counts = unique_class_counts(df_normalized, 'class')\n",
        "print(f\"Class counts in the DataFrame: {class_counts}\")\n",
        "for class_name, count in class_counts.items():\n",
        "    print(f\"{class_name}: {count}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 122,
      "metadata": {
        "id": "Q1QuttytjmZF"
      },
      "outputs": [],
      "source": [
        "# Assuming df is your original DataFrame with columns including 'class'\n",
        "# Load or define your DataFrame, adjust the filename or method accordingly\n",
        "\n",
        "# 1. Filter evil_twin instances\n",
        "df_evil_twin = df_normalized[df_normalized['class'] == 'evil_twin']\n",
        "\n",
        "# 2. Sample 60,000 normal instances randomly\n",
        "df_normal = df_normalized[df_normalized['class'] == 'normal'].sample(n=60000, random_state=42)\n",
        "\n",
        "# 3. Concatenate both DataFrames\n",
        "df_normalized = pd.concat([df_evil_twin, df_normal], ignore_index=True)\n",
        "\n",
        "# Optionally, you can shuffle the DataFrame if needed\n",
        "df_normalized = df_normalized.sample(frac=1, random_state=42).reset_index(drop=True)\n",
        "\n",
        "# Print the shape to verify\n",
        "#print(\"Shape of df_normalized:\", df_normalized.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 123,
      "metadata": {
        "id": "4i0ObVqGjobO",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "26bde533-45f6-4c30-f1db-0f8a550dad40"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Class counts in the DataFrame: {'normal': 60000, 'evil_twin': 50643}\n",
            "normal: 60000\n",
            "evil_twin: 50643\n"
          ]
        }
      ],
      "source": [
        "def unique_class_counts(df_normalized, target_column):\n",
        "    class_counts = df_normalized[target_column].value_counts().to_dict()\n",
        "    return class_counts\n",
        "\n",
        "# Get unique class counts\n",
        "class_counts = unique_class_counts(df_normalized, 'class')\n",
        "print(f\"Class counts in the DataFrame: {class_counts}\")\n",
        "for class_name, count in class_counts.items():\n",
        "    print(f\"{class_name}: {count}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 124,
      "metadata": {
        "id": "7__xK6a9jq0M"
      },
      "outputs": [],
      "source": [
        "from sklearn.utils import shuffle\n",
        "\n",
        "df_normalized = shuffle(df_normalized)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "366bumjbcURf"
      },
      "source": [
        "### 2.3.2 KNN Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 125,
      "metadata": {
        "id": "1VtIBc96cWiU",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "aef9adb0-9167-42b1-8983-402d83f00524"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "KNN Model Metrics - Training Set:\n",
            "Accuracy: 0.8860858169329145\n",
            "Recall: 0.8860858169329145\n",
            "F1-score: 0.8862843038626806\n",
            "Precision: 0.8885250166368893\n",
            "\n",
            "KNN Model Metrics - Testing Set:\n",
            "Accuracy: 0.8278277373582177\n",
            "Recall: 0.8278277373582177\n",
            "F1-score: 0.8281031421944228\n",
            "Precision: 0.8299970030064541\n"
          ]
        }
      ],
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.metrics import accuracy_score, recall_score, f1_score, precision_score\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "\n",
        "# Split the balanced data into features (X) and target variable (y)\n",
        "X = df_normalized.drop(columns=['class'])\n",
        "y = df_normalized['class']\n",
        "\n",
        "# Split the balanced data into training and testing sets (80% training, 20% testing)\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
        "\n",
        "# Create a KNN classifier\n",
        "knn_clf = KNeighborsClassifier(n_neighbors=5)  # You can adjust the number of neighbors\n",
        "\n",
        "# Train the KNN classifier on the training data\n",
        "knn_clf.fit(X_train, y_train)\n",
        "\n",
        "# Make predictions on the training data\n",
        "y_train_pred_knn = knn_clf.predict(X_train)\n",
        "\n",
        "# Make predictions on the testing data\n",
        "y_test_pred_knn = knn_clf.predict(X_test)\n",
        "\n",
        "# Evaluate the KNN classifier on the training data\n",
        "accuracy_train_knn = accuracy_score(y_train, y_train_pred_knn)\n",
        "recall_train_knn = recall_score(y_train, y_train_pred_knn, average='weighted')\n",
        "f1_train_knn = f1_score(y_train, y_train_pred_knn, average='weighted')\n",
        "precision_train_knn = precision_score(y_train, y_train_pred_knn, average='weighted')\n",
        "\n",
        "# Evaluate the KNN classifier on the testing data\n",
        "accuracy_test_knn = accuracy_score(y_test, y_test_pred_knn)\n",
        "recall_test_knn = recall_score(y_test, y_test_pred_knn, average='weighted')\n",
        "f1_test_knn = f1_score(y_test, y_test_pred_knn, average='weighted')\n",
        "precision_test_knn = precision_score(y_test, y_test_pred_knn, average='weighted')\n",
        "\n",
        "print(\"KNN Model Metrics - Training Set:\")\n",
        "print(\"Accuracy:\", accuracy_train_knn)\n",
        "print(\"Recall:\", recall_train_knn)\n",
        "print(\"F1-score:\", f1_train_knn)\n",
        "print(\"Precision:\", precision_train_knn)\n",
        "\n",
        "print(\"\\nKNN Model Metrics - Testing Set:\")\n",
        "print(\"Accuracy:\", accuracy_test_knn)\n",
        "print(\"Recall:\", recall_test_knn)\n",
        "print(\"F1-score:\", f1_test_knn)\n",
        "print(\"Precision:\", precision_test_knn)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 126,
      "metadata": {
        "id": "zfnwJ-5kjxiJ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "outputId": "bc02b75d-dca7-4fd2-fa98-c84e35bae99c"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Compute confusion matrix for testing data\n",
        "cm = confusion_matrix(y_test, y_test_pred_knn)\n",
        "\n",
        "\n",
        "# Plot confusion matrix\n",
        "plt.figure(figsize=(8, 6))\n",
        "sns.heatmap(cm, annot=True, cmap='Blues', fmt='g',\n",
        "            xticklabels=['normal', 'evil_twin'],\n",
        "            yticklabels=['normal', 'evil_twin'])\n",
        "plt.xlabel('Predicted')\n",
        "plt.ylabel('Actual')\n",
        "plt.title('Confusion Matrix - KNN Model')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 127,
      "metadata": {
        "id": "iUtqsetIkBZc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 927
        },
        "outputId": "9a9296bc-3702-4487-a759-63a180bc16f6"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.model_selection import learning_curve\n",
        "\n",
        "def plot_learning_curve_knn(estimator, title, X, y, ylim=None, cv=None,\n",
        "                            n_jobs=None, train_sizes=np.linspace(.1, 1.0, 5)):\n",
        "    plt.figure()\n",
        "    plt.title(title)\n",
        "    if ylim is not None:\n",
        "        plt.ylim(*ylim)\n",
        "    plt.xlabel(\"Training examples\")\n",
        "    plt.ylabel(\"Score\")\n",
        "    train_sizes, train_scores, test_scores = learning_curve(\n",
        "        estimator, X, y, cv=cv, n_jobs=n_jobs, train_sizes=train_sizes)\n",
        "    train_scores_mean = np.mean(train_scores, axis=1)\n",
        "    train_scores_std = np.std(train_scores, axis=1)\n",
        "    test_scores_mean = np.mean(test_scores, axis=1)\n",
        "    test_scores_std = np.std(test_scores, axis=1)\n",
        "    plt.grid()\n",
        "\n",
        "    plt.fill_between(train_sizes, train_scores_mean - train_scores_std,\n",
        "                     train_scores_mean + train_scores_std, alpha=0.1,\n",
        "                     color=\"r\")\n",
        "    plt.fill_between(train_sizes, test_scores_mean - test_scores_std,\n",
        "                     test_scores_mean + test_scores_std, alpha=0.1, color=\"g\")\n",
        "    plt.plot(train_sizes, train_scores_mean, 'o-', color=\"r\",\n",
        "             label=\"Training score\")\n",
        "    plt.plot(train_sizes, test_scores_mean, 'o-', color=\"g\",\n",
        "             label=\"Cross-validation score\")\n",
        "\n",
        "    plt.legend(loc=\"best\")\n",
        "    return plt\n",
        "\n",
        "# Use the KNeighborsClassifier for demonstration\n",
        "knn_classifier = KNeighborsClassifier(n_neighbors=5)  # Adjust the number of neighbors as needed\n",
        "\n",
        "# Plot learning curves for training set\n",
        "title = \"Learning Curves - Training Set (KNN)\"\n",
        "plot_learning_curve_knn(knn_classifier, title, X_train, y_train, cv=5)\n",
        "plt.show()\n",
        "\n",
        "# Plot learning curves for testing set\n",
        "title = \"Learning Curves - Testing Set (KNN)\"\n",
        "plot_learning_curve_knn(knn_classifier, title, X_test, y_test, cv=5)\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zETFhdS9cYAq"
      },
      "source": [
        "### 2.3.3 Navie Bayes Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 128,
      "metadata": {
        "id": "vJjZ6nhCkRnP",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7ee81007-36b1-41fe-e994-1ee43734ed1e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Naive Bayes Model Metrics - Training Set:\n",
            "Accuracy: 0.5357909483245589\n",
            "Recall: 0.5357909483245589\n",
            "F1-score: 0.4641930070192118\n",
            "Precision: 0.5134578341545815\n",
            "\n",
            "Naive Bayes Model Metrics - Testing Set:\n",
            "Accuracy: 0.5336436350490307\n",
            "Recall: 0.5336436350490307\n",
            "F1-score: 0.46339515987710417\n",
            "Precision: 0.5114427651673973\n"
          ]
        }
      ],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.naive_bayes import GaussianNB\n",
        "from sklearn.metrics import accuracy_score, recall_score, f1_score, precision_score\n",
        "\n",
        "# Split the balanced data into features (X) and target variable (y)\n",
        "X = df_normalized.drop(columns=['class'])\n",
        "y = df_normalized['class']\n",
        "\n",
        "# Split the data into training and testing sets (80% training, 20% testing)\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
        "\n",
        "# Create a Naive Bayes classifier\n",
        "nb_clf = GaussianNB()\n",
        "\n",
        "# Train the classifier on the training data\n",
        "nb_clf.fit(X_train, y_train)\n",
        "\n",
        "# Make predictions on both the training and testing data\n",
        "y_train_pred = nb_clf.predict(X_train)\n",
        "y_test_pred = nb_clf.predict(X_test)\n",
        "\n",
        "# Evaluate the Naive Bayes classifier on the training data\n",
        "accuracy_train = accuracy_score(y_train, y_train_pred)\n",
        "recall_train = recall_score(y_train, y_train_pred, average='weighted')\n",
        "f1_train = f1_score(y_train, y_train_pred, average='weighted')\n",
        "precision_train = precision_score(y_train, y_train_pred, average='weighted')\n",
        "\n",
        "# Evaluate the Naive Bayes classifier on the testing data\n",
        "accuracy_test = accuracy_score(y_test, y_test_pred)\n",
        "recall_test = recall_score(y_test, y_test_pred, average='weighted')\n",
        "f1_test = f1_score(y_test, y_test_pred, average='weighted')\n",
        "precision_test = precision_score(y_test, y_test_pred, average='weighted')\n",
        "\n",
        "# Print the metrics for training data\n",
        "print(\"Naive Bayes Model Metrics - Training Set:\")\n",
        "print(\"Accuracy:\", accuracy_train)\n",
        "print(\"Recall:\", recall_train)\n",
        "print(\"F1-score:\", f1_train)\n",
        "print(\"Precision:\", precision_train)\n",
        "\n",
        "# Print the metrics for testing data\n",
        "print(\"\\nNaive Bayes Model Metrics - Testing Set:\")\n",
        "print(\"Accuracy:\", accuracy_test)\n",
        "print(\"Recall:\", recall_test)\n",
        "print(\"F1-score:\", f1_test)\n",
        "print(\"Precision:\", precision_test)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 129,
      "metadata": {
        "id": "wXa8K4yZkTh_",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "outputId": "73f66405-5916-49fd-c703-951dffeb4017"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Compute confusion matrix\n",
        "cm = confusion_matrix(y_test, y_pred)\n",
        "\n",
        "# Plot confusion matrix\n",
        "plt.figure(figsize=(8, 6))\n",
        "sns.heatmap(cm, annot=True, cmap='Blues', fmt='g',\n",
        "            xticklabels=['normal', 'evil_twin'],\n",
        "            yticklabels=['normal', 'evil_twin'])\n",
        "plt.xlabel('Predicted')\n",
        "plt.ylabel('Actual')\n",
        "plt.title('Confusion Matrix - Naive Bayes Model')\n",
        "plt.show()"
      ]
    }
  ],
  "metadata": {
    "accelerator": "TPU",
    "colab": {
      "collapsed_sections": [
        "dyzmlqevfNRz",
        "k6qdieKX-nQ8",
        "G-ZC4860AGFK",
        "aKjpPBBG1wFm",
        "366bumjbcURf",
        "zETFhdS9cYAq"
      ],
      "gpuType": "V28",
      "provenance": [],
      "mount_file_id": "1a7TQbRGazJwzzqSOk9zmVwlh-yiP4dzz",
      "authorship_tag": "ABX9TyMvI35etPJUvifvmkod1vol",
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}