BB84QuNetSim.ipynb

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  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5lFOxEIpO6Vy",
        "outputId": "c7acf13d-8f2f-45cf-f32c-958023198ebc"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting qunetsim\n",
            "  Downloading qunetsim-0.1.3.post1-py3-none-any.whl (60 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m60.5/60.5 kB\u001b[0m \u001b[31m1.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting eqsn (from qunetsim)\n",
            "  Downloading EQSN-0.0.8.tar.gz (9.8 kB)\n",
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from qunetsim) (3.3)\n",
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            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.10/dist-packages (from qunetsim) (3.7.1)\n",
            "Collecting pathvalidate (from qunetsim)\n",
            "  Downloading pathvalidate-3.2.0-py3-none-any.whl (23 kB)\n",
            "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->qunetsim) (1.2.1)\n",
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            "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->qunetsim) (9.4.0)\n",
            "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->qunetsim) (3.1.2)\n",
            "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.10/dist-packages (from matplotlib->qunetsim) (2.8.2)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.7->matplotlib->qunetsim) (1.16.0)\n",
            "Building wheels for collected packages: eqsn\n",
            "  Building wheel for eqsn (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for eqsn: filename=EQSN-0.0.8-py3-none-any.whl size=11939 sha256=56872d66cefdb745611308e42b3d24f4734fe1ad45ed766123a76798eb99cf32\n",
            "  Stored in directory: /root/.cache/pip/wheels/8a/08/eb/10ad0fdccd7b467213f43da4dbc9c793c1b31259681cc92d7a\n",
            "Successfully built eqsn\n",
            "Installing collected packages: pathvalidate, eqsn, qunetsim\n",
            "Successfully installed eqsn-0.0.8 pathvalidate-3.2.0 qunetsim-0.1.3.post1\n"
          ]
        }
      ],
      "source": [
        "!pip install qunetsim"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from qunetsim.components import Host, Network\n",
        "from qunetsim.objects import Qubit\n",
        "import random\n",
        "import numpy as np"
      ],
      "metadata": {
        "id": "fJzLQ6pJRoef"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class BB84Simulation:\n",
        "    def __init__(self, num_bits):\n",
        "        self.num_bits = num_bits\n",
        "        self.network = Network.get_instance()\n",
        "        self.network.start(['Alice', 'Bob', 'Eve'])\n",
        "        self.alice = Host('Alice')\n",
        "        self.bob = Host('Bob')\n",
        "        self.eve = Host('Eve')\n",
        "        self.alice.start()\n",
        "        self.bob.start()\n",
        "        self.eve.start()\n",
        "\n",
        "    def generate_bits_and_basis(self):\n",
        "        bits = [random.randint(0, 1) for _ in range(self.num_bits)]\n",
        "        basis = [random.choice(['H', 'V']) for _ in range(self.num_bits)]\n",
        "        return bits, basis\n",
        "\n",
        "    def encode_qubit(self, host, bit, basis):\n",
        "        q = Qubit(host)\n",
        "        if basis == 'H':\n",
        "            if bit == 1:\n",
        "                q.X()\n",
        "        elif basis == 'V':\n",
        "            q.H()\n",
        "            if bit == 1:\n",
        "                q.X()\n",
        "        return q\n",
        "\n",
        "    def simulate_eavesdropping(self, qubit, basis):\n",
        "        if basis == 'V':\n",
        "            return random.choice([0, 1])\n",
        "        return random.choice([0, 1])\n",
        "\n",
        "    def measure_qubit(self, qubit, basis):\n",
        "        if basis == 'V':\n",
        "            qubit.H()\n",
        "        measured_result = qubit.measure()\n",
        "        return measured_result\n",
        "\n",
        "    def run_simulation(self, with_eavesdropper=False):\n",
        "        alice_bits, alice_basis = self.generate_bits_and_basis()\n",
        "        bob_basis = self.generate_bits_and_basis()[1]\n",
        "\n",
        "        # Encode qubits at Alice's side\n",
        "        alice_to_bob_qubits = [self.encode_qubit(self.alice, bit, basis) for bit, basis in zip(alice_bits, alice_basis)]\n",
        "        eve_measurements = []\n",
        "\n",
        "        if with_eavesdropper:\n",
        "            eve_measurements = [self.simulate_eavesdropping(q, alice_basis[i]) for i, q in enumerate(alice_to_bob_qubits) if alice_basis[i] == bob_basis[i]]\n",
        "\n",
        "        # Bob measures the qubits received\n",
        "        bob_measurements = [self.measure_qubit(q, basis) for q, basis in zip(alice_to_bob_qubits, bob_basis)]\n",
        "\n",
        "        # Compare bases to get matched bits\n",
        "        matched_indices, alice_matched_bits, bob_matched_bits = self.compare_bases(alice_basis, bob_basis, alice_bits, bob_measurements)\n",
        "        error_rate = self.calculate_error_rate(alice_matched_bits, bob_matched_bits)\n",
        "\n",
        "        features = extract_features(alice_matched_bits, bob_matched_bits, error_rate, with_eavesdropper, eve_measurements)\n",
        "        return features\n",
        "    def compare_bases(self, alice_basis, bob_basis, alice_bits, bob_measurements):\n",
        "        matched_indices = [i for i in range(len(alice_basis)) if alice_basis[i] == bob_basis[i]]\n",
        "        alice_matched_bits = [alice_bits[i] for i in matched_indices]\n",
        "        bob_matched_bits = [bob_measurements[i] for i in matched_indices]\n",
        "        return matched_indices, alice_matched_bits, bob_matched_bits\n",
        "\n",
        "    def calculate_error_rate(self, alice_matched_bits, bob_matched_bits):\n",
        "        errors = sum(a != b for a, b in zip(alice_matched_bits, bob_matched_bits))\n",
        "        error_rate = errors / len(alice_matched_bits) if alice_matched_bits else 0\n",
        "        return error_rate\n",
        "\n",
        "    def stop(self):\n",
        "        self.network.stop()\n",
        "        self.alice.stop()\n",
        "        self.bob.stop()\n",
        "        self.eve.stop()\n"
      ],
      "metadata": {
        "id": "uiIC_8FCo6g3"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def extract_features(alice_matched_bits, bob_matched_bits, error_rate, include_eve, eve_measurements=None):\n",
        "    initial_bits = np.array(alice_matched_bits)\n",
        "    final_key = np.array([0 if bit == 0 else 1 for bit in alice_matched_bits])\n",
        "    error_correction_rate = 1 - error_rate\n",
        "    parity_a = np.sum(initial_bits) % 2\n",
        "    v_bit_probability = np.sum(initial_bits) / len(alice_matched_bits) if alice_matched_bits else 0\n",
        "    base_mismatch_percentage = np.sum(initial_bits != np.array(bob_matched_bits)) / len(alice_matched_bits) if alice_matched_bits else 0\n",
        "\n",
        "    if include_eve and eve_measurements is not None:\n",
        "        eve_mismatch_percentage = np.sum(initial_bits != np.array(eve_measurements)) / len(alice_matched_bits) if alice_matched_bits else 0\n",
        "        hamming_distance_ae = np.sum(initial_bits != np.array(eve_measurements))\n",
        "        key_agreement_ratio_ae = np.sum(final_key == np.array(eve_measurements)) / len(alice_matched_bits) if alice_matched_bits else 0\n",
        "    else:\n",
        "        eve_mismatch_percentage = -1  # Indicates absence or irrelevance of Eve's measurements\n",
        "        hamming_distance_ae = -1  # Same as above\n",
        "        key_agreement_ratio_ae = -1  # Same as above\n",
        "\n",
        "    hamming_distance_ab = np.sum(initial_bits != np.array(bob_matched_bits))\n",
        "    key_agreement_ratio_ab = np.sum(final_key == np.array(bob_matched_bits)) / len(alice_matched_bits) if alice_matched_bits else 0\n",
        "\n",
        "    features = [\n",
        "        *initial_bits.tolist(),\n",
        "        *final_key.tolist(),\n",
        "        error_correction_rate,\n",
        "        parity_a,\n",
        "        v_bit_probability,\n",
        "        base_mismatch_percentage,\n",
        "        eve_mismatch_percentage,\n",
        "        hamming_distance_ab,\n",
        "        hamming_distance_ae,\n",
        "        key_agreement_ratio_ab,\n",
        "        key_agreement_ratio_ae\n",
        "    ]\n",
        "\n",
        "    return features"
      ],
      "metadata": {
        "id": "Xx4zcmtopA6c"
      },
      "execution_count": 88,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "num_simulations = 100\n",
        "features_with_eve = []\n",
        "features_without_eve = []\n",
        "\n",
        "bb84_sim = BB84Simulation(10)\n",
        "\n",
        "for _ in range(num_simulations):\n",
        "    features_with_eve.append(bb84_sim.run_simulation(with_eavesdropper=True))\n",
        "    features_without_eve.append(bb84_sim.run_simulation(with_eavesdropper=False))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cGWksPzTWnG5",
        "outputId": "d96d0017-70da-4e3b-e038-2a1b81a833fd"
      },
      "execution_count": 129,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n",
            "  self.pid = os.fork()\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def flatten_features(features):\n",
        "    standardized_length = 20\n",
        "    flattened_features = []\n",
        "\n",
        "    for feature in features:\n",
        "        if any(isinstance(i, list) for i in feature):\n",
        "            flattened = [item for sublist in feature for item in (sublist if isinstance(sublist, list) else [sublist])]\n",
        "        else:\n",
        "            flattened = feature\n",
        "\n",
        "\n",
        "        if len(flattened) < standardized_length:\n",
        "            flattened.extend([-1] * (standardized_length - len(flattened)))\n",
        "\n",
        "\n",
        "        flattened = flattened[:standardized_length]\n",
        "\n",
        "\n",
        "        flattened = [float(item) for item in flattened]\n",
        "\n",
        "        flattened_features.append(flattened)\n",
        "\n",
        "    return np.array(flattened_features)\n",
        "\n",
        "X_without_eve = flatten_features(features_without_eve)\n",
        "y_without_eve = np.array([0]*len(features_without_eve))\n",
        "X_with_eve = flatten_features(features_with_eve)\n",
        "y_with_eve = np.array([1] * len(features_with_eve))\n",
        "\n",
        "X = np.concatenate((X_without_eve, X_with_eve), axis=0)\n",
        "y = np.concatenate((y_without_eve, y_with_eve), axis=0)"
      ],
      "metadata": {
        "id": "Bc-GY3AEpGCc"
      },
      "execution_count": 139,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Dense\n",
        "\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
        "\n",
        "scaler = StandardScaler()\n",
        "X_train = scaler.fit_transform(X_train)\n",
        "X_test = scaler.transform(X_test)\n",
        "\n",
        "model = Sequential([\n",
        "    Dense(64, input_shape=(X_train.shape[1],), activation='relu'),\n",
        "    Dense(32, activation='relu'),\n",
        "    Dense(1, activation='sigmoid')\n",
        "])\n",
        "\n",
        "model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n",
        "model.fit(X_train, y_train, epochs=120, batch_size=32, validation_split=0.1)"
      ],
      "metadata": {
        "id": "QHTsB9c2ptAk",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "81568eac-4839-4496-b3c4-eeb06083c98a"
      },
      "execution_count": 158,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/120\n",
            "5/5 [==============================] - 4s 88ms/step - loss: 0.7257 - accuracy: 0.5764 - val_loss: 0.6789 - val_accuracy: 0.6250\n",
            "Epoch 2/120\n",
            "5/5 [==============================] - 0s 19ms/step - loss: 0.6180 - accuracy: 0.6806 - val_loss: 0.6131 - val_accuracy: 0.6250\n",
            "Epoch 3/120\n",
            "5/5 [==============================] - 0s 20ms/step - loss: 0.5516 - accuracy: 0.7986 - val_loss: 0.5640 - val_accuracy: 0.6875\n",
            "Epoch 4/120\n",
            "5/5 [==============================] - 0s 20ms/step - loss: 0.4986 - accuracy: 0.8333 - val_loss: 0.5280 - val_accuracy: 0.7500\n",
            "Epoch 5/120\n",
            "5/5 [==============================] - 0s 19ms/step - loss: 0.4543 - accuracy: 0.8542 - val_loss: 0.4995 - val_accuracy: 0.7500\n",
            "Epoch 6/120\n",
            "5/5 [==============================] - 0s 20ms/step - loss: 0.4193 - accuracy: 0.8819 - val_loss: 0.4746 - val_accuracy: 0.7500\n",
            "Epoch 7/120\n",
            "5/5 [==============================] - 0s 20ms/step - loss: 0.3864 - accuracy: 0.9306 - val_loss: 0.4483 - val_accuracy: 0.7500\n",
            "Epoch 8/120\n",
            "5/5 [==============================] - 0s 25ms/step - loss: 0.3550 - accuracy: 0.9514 - val_loss: 0.4162 - val_accuracy: 0.7500\n",
            "Epoch 9/120\n",
            "5/5 [==============================] - 0s 19ms/step - loss: 0.3259 - accuracy: 0.9514 - val_loss: 0.3818 - val_accuracy: 0.8125\n",
            "Epoch 10/120\n",
            "5/5 [==============================] - 0s 10ms/step - loss: 0.3001 - accuracy: 0.9444 - val_loss: 0.3480 - val_accuracy: 0.9375\n",
            "Epoch 11/120\n",
            "5/5 [==============================] - 0s 10ms/step - loss: 0.2780 - accuracy: 0.9514 - val_loss: 0.3174 - val_accuracy: 1.0000\n",
            "Epoch 12/120\n",
            "5/5 [==============================] - 0s 15ms/step - loss: 0.2568 - accuracy: 0.9653 - val_loss: 0.2886 - val_accuracy: 1.0000\n",
            "Epoch 13/120\n",
            "5/5 [==============================] - 0s 15ms/step - loss: 0.2375 - accuracy: 0.9653 - val_loss: 0.2579 - val_accuracy: 1.0000\n",
            "Epoch 14/120\n",
            "5/5 [==============================] - 0s 15ms/step - loss: 0.2209 - accuracy: 0.9653 - val_loss: 0.2342 - val_accuracy: 1.0000\n",
            "Epoch 15/120\n",
            "5/5 [==============================] - 0s 11ms/step - loss: 0.2036 - accuracy: 0.9653 - val_loss: 0.2156 - val_accuracy: 1.0000\n",
            "Epoch 16/120\n",
            "5/5 [==============================] - 0s 14ms/step - loss: 0.1893 - accuracy: 0.9653 - val_loss: 0.1990 - val_accuracy: 1.0000\n",
            "Epoch 17/120\n",
            "5/5 [==============================] - 0s 10ms/step - loss: 0.1752 - accuracy: 0.9653 - val_loss: 0.1827 - val_accuracy: 1.0000\n",
            "Epoch 18/120\n",
            "5/5 [==============================] - 0s 14ms/step - loss: 0.1631 - accuracy: 0.9653 - val_loss: 0.1684 - val_accuracy: 1.0000\n",
            "Epoch 19/120\n",
            "5/5 [==============================] - 0s 10ms/step - loss: 0.1507 - accuracy: 0.9722 - val_loss: 0.1543 - val_accuracy: 1.0000\n",
            "Epoch 20/120\n",
            "5/5 [==============================] - 0s 10ms/step - loss: 0.1396 - accuracy: 0.9722 - val_loss: 0.1385 - val_accuracy: 1.0000\n",
            "Epoch 21/120\n",
            "5/5 [==============================] - 0s 10ms/step - loss: 0.1298 - accuracy: 0.9722 - val_loss: 0.1251 - val_accuracy: 1.0000\n",
            "Epoch 22/120\n",
            "5/5 [==============================] - 0s 11ms/step - loss: 0.1187 - accuracy: 0.9792 - val_loss: 0.1143 - val_accuracy: 1.0000\n",
            "Epoch 23/120\n",
            "5/5 [==============================] - 0s 9ms/step - loss: 0.1090 - accuracy: 0.9792 - val_loss: 0.1023 - val_accuracy: 1.0000\n",
            "Epoch 24/120\n",
            "5/5 [==============================] - 0s 15ms/step - loss: 0.1013 - accuracy: 0.9861 - val_loss: 0.0918 - val_accuracy: 1.0000\n",
            "Epoch 25/120\n",
            "5/5 [==============================] - 0s 9ms/step - loss: 0.0927 - accuracy: 0.9861 - val_loss: 0.0822 - val_accuracy: 1.0000\n",
            "Epoch 26/120\n",
            "5/5 [==============================] - 0s 10ms/step - loss: 0.0855 - accuracy: 0.9931 - val_loss: 0.0731 - val_accuracy: 1.0000\n",
            "Epoch 27/120\n",
            "5/5 [==============================] - 0s 15ms/step - loss: 0.0784 - accuracy: 0.9931 - val_loss: 0.0660 - val_accuracy: 1.0000\n",
            "Epoch 28/120\n",
            "5/5 [==============================] - 0s 15ms/step - loss: 0.0724 - accuracy: 0.9931 - val_loss: 0.0591 - val_accuracy: 1.0000\n",
            "Epoch 29/120\n",
            "5/5 [==============================] - 0s 10ms/step - loss: 0.0668 - accuracy: 0.9931 - val_loss: 0.0542 - val_accuracy: 1.0000\n",
            "Epoch 30/120\n",
            "5/5 [==============================] - 0s 15ms/step - loss: 0.0618 - accuracy: 0.9931 - val_loss: 0.0491 - val_accuracy: 1.0000\n",
            "Epoch 31/120\n",
            "5/5 [==============================] - 0s 10ms/step - loss: 0.0564 - accuracy: 0.9931 - val_loss: 0.0454 - val_accuracy: 1.0000\n",
            "Epoch 32/120\n",
            "5/5 [==============================] - 0s 10ms/step - loss: 0.0519 - accuracy: 0.9931 - val_loss: 0.0408 - val_accuracy: 1.0000\n",
            "Epoch 33/120\n",
            "5/5 [==============================] - 0s 11ms/step - loss: 0.0476 - accuracy: 0.9931 - val_loss: 0.0369 - val_accuracy: 1.0000\n",
            "Epoch 34/120\n",
            "5/5 [==============================] - 0s 14ms/step - loss: 0.0443 - accuracy: 0.9931 - val_loss: 0.0336 - val_accuracy: 1.0000\n",
            "Epoch 35/120\n",
            "5/5 [==============================] - 0s 10ms/step - loss: 0.0409 - accuracy: 1.0000 - val_loss: 0.0312 - val_accuracy: 1.0000\n",
            "Epoch 36/120\n",
            "5/5 [==============================] - 0s 10ms/step - loss: 0.0376 - accuracy: 1.0000 - val_loss: 0.0287 - val_accuracy: 1.0000\n",
            "Epoch 37/120\n",
            "5/5 [==============================] - 0s 10ms/step - loss: 0.0350 - accuracy: 1.0000 - val_loss: 0.0265 - val_accuracy: 1.0000\n",
            "Epoch 38/120\n",
            "5/5 [==============================] - 0s 11ms/step - loss: 0.0323 - accuracy: 1.0000 - val_loss: 0.0244 - val_accuracy: 1.0000\n",
            "Epoch 39/120\n",
            "5/5 [==============================] - 0s 10ms/step - loss: 0.0301 - accuracy: 1.0000 - val_loss: 0.0224 - val_accuracy: 1.0000\n",
            "Epoch 40/120\n",
            "5/5 [==============================] - 0s 10ms/step - loss: 0.0280 - accuracy: 1.0000 - val_loss: 0.0207 - val_accuracy: 1.0000\n",
            "Epoch 41/120\n",
            "5/5 [==============================] - 0s 9ms/step - loss: 0.0260 - accuracy: 1.0000 - val_loss: 0.0192 - val_accuracy: 1.0000\n",
            "Epoch 42/120\n",
            "5/5 [==============================] - 0s 15ms/step - loss: 0.0244 - accuracy: 1.0000 - val_loss: 0.0182 - val_accuracy: 1.0000\n",
            "Epoch 43/120\n",
            "5/5 [==============================] - 0s 10ms/step - loss: 0.0227 - accuracy: 1.0000 - val_loss: 0.0172 - val_accuracy: 1.0000\n",
            "Epoch 44/120\n",
            "5/5 [==============================] - 0s 15ms/step - loss: 0.0211 - accuracy: 1.0000 - val_loss: 0.0160 - val_accuracy: 1.0000\n",
            "Epoch 45/120\n",
            "5/5 [==============================] - 0s 11ms/step - loss: 0.0197 - accuracy: 1.0000 - val_loss: 0.0148 - val_accuracy: 1.0000\n",
            "Epoch 46/120\n",
            "5/5 [==============================] - 0s 10ms/step - loss: 0.0185 - accuracy: 1.0000 - val_loss: 0.0138 - val_accuracy: 1.0000\n",
            "Epoch 47/120\n",
            "5/5 [==============================] - 0s 14ms/step - loss: 0.0174 - accuracy: 1.0000 - val_loss: 0.0129 - val_accuracy: 1.0000\n",
            "Epoch 48/120\n",
            "5/5 [==============================] - 0s 10ms/step - loss: 0.0163 - accuracy: 1.0000 - val_loss: 0.0122 - val_accuracy: 1.0000\n",
            "Epoch 49/120\n",
            "5/5 [==============================] - 0s 15ms/step - loss: 0.0153 - accuracy: 1.0000 - val_loss: 0.0114 - val_accuracy: 1.0000\n",
            "Epoch 50/120\n",
            "5/5 [==============================] - 0s 11ms/step - loss: 0.0144 - accuracy: 1.0000 - val_loss: 0.0106 - val_accuracy: 1.0000\n",
            "Epoch 51/120\n",
            "5/5 [==============================] - 0s 11ms/step - loss: 0.0136 - accuracy: 1.0000 - val_loss: 0.0100 - val_accuracy: 1.0000\n",
            "Epoch 52/120\n",
            "5/5 [==============================] - 0s 10ms/step - loss: 0.0128 - accuracy: 1.0000 - val_loss: 0.0095 - val_accuracy: 1.0000\n",
            "Epoch 53/120\n",
            "5/5 [==============================] - 0s 10ms/step - loss: 0.0122 - accuracy: 1.0000 - val_loss: 0.0090 - val_accuracy: 1.0000\n",
            "Epoch 54/120\n",
            "5/5 [==============================] - 0s 14ms/step - loss: 0.0115 - accuracy: 1.0000 - val_loss: 0.0084 - val_accuracy: 1.0000\n",
            "Epoch 55/120\n",
            "5/5 [==============================] - 0s 11ms/step - loss: 0.0109 - accuracy: 1.0000 - val_loss: 0.0080 - val_accuracy: 1.0000\n",
            "Epoch 56/120\n",
            "5/5 [==============================] - 0s 10ms/step - loss: 0.0103 - accuracy: 1.0000 - val_loss: 0.0077 - val_accuracy: 1.0000\n",
            "Epoch 57/120\n",
            "5/5 [==============================] - 0s 10ms/step - loss: 0.0098 - accuracy: 1.0000 - val_loss: 0.0073 - val_accuracy: 1.0000\n",
            "Epoch 58/120\n",
            "5/5 [==============================] - 0s 14ms/step - loss: 0.0093 - accuracy: 1.0000 - val_loss: 0.0070 - val_accuracy: 1.0000\n",
            "Epoch 59/120\n",
            "5/5 [==============================] - 0s 10ms/step - loss: 0.0089 - accuracy: 1.0000 - val_loss: 0.0066 - val_accuracy: 1.0000\n",
            "Epoch 60/120\n",
            "5/5 [==============================] - 0s 13ms/step - loss: 0.0085 - accuracy: 1.0000 - val_loss: 0.0064 - val_accuracy: 1.0000\n",
            "Epoch 61/120\n",
            "5/5 [==============================] - 0s 10ms/step - loss: 0.0081 - accuracy: 1.0000 - val_loss: 0.0061 - val_accuracy: 1.0000\n",
            "Epoch 62/120\n",
            "5/5 [==============================] - 0s 14ms/step - loss: 0.0077 - accuracy: 1.0000 - val_loss: 0.0058 - val_accuracy: 1.0000\n",
            "Epoch 63/120\n",
            "5/5 [==============================] - 0s 10ms/step - loss: 0.0074 - accuracy: 1.0000 - val_loss: 0.0056 - val_accuracy: 1.0000\n",
            "Epoch 64/120\n",
            "5/5 [==============================] - 0s 10ms/step - loss: 0.0071 - accuracy: 1.0000 - val_loss: 0.0054 - val_accuracy: 1.0000\n",
            "Epoch 65/120\n",
            "5/5 [==============================] - 0s 15ms/step - loss: 0.0068 - accuracy: 1.0000 - val_loss: 0.0052 - val_accuracy: 1.0000\n",
            "Epoch 66/120\n",
            "5/5 [==============================] - 0s 10ms/step - loss: 0.0065 - accuracy: 1.0000 - val_loss: 0.0050 - val_accuracy: 1.0000\n",
            "Epoch 67/120\n",
            "5/5 [==============================] - 0s 10ms/step - loss: 0.0063 - accuracy: 1.0000 - val_loss: 0.0049 - val_accuracy: 1.0000\n",
            "Epoch 68/120\n",
            "5/5 [==============================] - 0s 14ms/step - loss: 0.0060 - accuracy: 1.0000 - val_loss: 0.0047 - val_accuracy: 1.0000\n",
            "Epoch 69/120\n",
            "5/5 [==============================] - 0s 12ms/step - loss: 0.0058 - accuracy: 1.0000 - val_loss: 0.0045 - val_accuracy: 1.0000\n",
            "Epoch 70/120\n",
            "5/5 [==============================] - 0s 15ms/step - loss: 0.0055 - accuracy: 1.0000 - val_loss: 0.0044 - val_accuracy: 1.0000\n",
            "Epoch 71/120\n",
            "5/5 [==============================] - 0s 10ms/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 0.0043 - val_accuracy: 1.0000\n",
            "Epoch 72/120\n",
            "5/5 [==============================] - 0s 10ms/step - loss: 0.0051 - accuracy: 1.0000 - val_loss: 0.0042 - val_accuracy: 1.0000\n",
            "Epoch 73/120\n",
            "5/5 [==============================] - 0s 15ms/step - loss: 0.0049 - accuracy: 1.0000 - val_loss: 0.0040 - val_accuracy: 1.0000\n",
            "Epoch 74/120\n",
            "5/5 [==============================] - 0s 15ms/step - loss: 0.0048 - accuracy: 1.0000 - val_loss: 0.0039 - val_accuracy: 1.0000\n",
            "Epoch 75/120\n",
            "5/5 [==============================] - 0s 11ms/step - loss: 0.0046 - accuracy: 1.0000 - val_loss: 0.0037 - val_accuracy: 1.0000\n",
            "Epoch 76/120\n",
            "5/5 [==============================] - 0s 11ms/step - loss: 0.0044 - accuracy: 1.0000 - val_loss: 0.0036 - val_accuracy: 1.0000\n",
            "Epoch 77/120\n",
            "5/5 [==============================] - 0s 13ms/step - loss: 0.0043 - accuracy: 1.0000 - val_loss: 0.0035 - val_accuracy: 1.0000\n",
            "Epoch 78/120\n",
            "5/5 [==============================] - 0s 10ms/step - loss: 0.0041 - accuracy: 1.0000 - val_loss: 0.0034 - val_accuracy: 1.0000\n",
            "Epoch 79/120\n",
            "5/5 [==============================] - 0s 14ms/step - loss: 0.0040 - accuracy: 1.0000 - val_loss: 0.0033 - val_accuracy: 1.0000\n",
            "Epoch 80/120\n",
            "5/5 [==============================] - 0s 15ms/step - loss: 0.0039 - accuracy: 1.0000 - val_loss: 0.0033 - val_accuracy: 1.0000\n",
            "Epoch 81/120\n",
            "5/5 [==============================] - 0s 11ms/step - loss: 0.0037 - accuracy: 1.0000 - val_loss: 0.0032 - val_accuracy: 1.0000\n",
            "Epoch 82/120\n",
            "5/5 [==============================] - 0s 10ms/step - loss: 0.0036 - accuracy: 1.0000 - val_loss: 0.0031 - val_accuracy: 1.0000\n",
            "Epoch 83/120\n",
            "5/5 [==============================] - 0s 14ms/step - loss: 0.0035 - accuracy: 1.0000 - val_loss: 0.0030 - val_accuracy: 1.0000\n",
            "Epoch 84/120\n",
            "5/5 [==============================] - 0s 10ms/step - loss: 0.0034 - accuracy: 1.0000 - val_loss: 0.0029 - val_accuracy: 1.0000\n",
            "Epoch 85/120\n",
            "5/5 [==============================] - 0s 11ms/step - loss: 0.0033 - accuracy: 1.0000 - val_loss: 0.0028 - val_accuracy: 1.0000\n",
            "Epoch 86/120\n",
            "5/5 [==============================] - 0s 10ms/step - loss: 0.0032 - accuracy: 1.0000 - val_loss: 0.0027 - val_accuracy: 1.0000\n",
            "Epoch 87/120\n",
            "5/5 [==============================] - 0s 10ms/step - loss: 0.0031 - accuracy: 1.0000 - val_loss: 0.0027 - val_accuracy: 1.0000\n",
            "Epoch 88/120\n",
            "5/5 [==============================] - 0s 10ms/step - loss: 0.0030 - accuracy: 1.0000 - val_loss: 0.0026 - val_accuracy: 1.0000\n",
            "Epoch 89/120\n",
            "5/5 [==============================] - 0s 14ms/step - loss: 0.0030 - accuracy: 1.0000 - val_loss: 0.0025 - val_accuracy: 1.0000\n",
            "Epoch 90/120\n",
            "5/5 [==============================] - 0s 14ms/step - loss: 0.0029 - accuracy: 1.0000 - val_loss: 0.0025 - val_accuracy: 1.0000\n",
            "Epoch 91/120\n",
            "5/5 [==============================] - 0s 11ms/step - loss: 0.0028 - accuracy: 1.0000 - val_loss: 0.0024 - val_accuracy: 1.0000\n",
            "Epoch 92/120\n",
            "5/5 [==============================] - 0s 10ms/step - loss: 0.0027 - accuracy: 1.0000 - val_loss: 0.0023 - val_accuracy: 1.0000\n",
            "Epoch 93/120\n",
            "5/5 [==============================] - 0s 17ms/step - loss: 0.0026 - accuracy: 1.0000 - val_loss: 0.0023 - val_accuracy: 1.0000\n",
            "Epoch 94/120\n",
            "5/5 [==============================] - 0s 13ms/step - loss: 0.0026 - accuracy: 1.0000 - val_loss: 0.0022 - val_accuracy: 1.0000\n",
            "Epoch 95/120\n",
            "5/5 [==============================] - 0s 11ms/step - loss: 0.0025 - accuracy: 1.0000 - val_loss: 0.0022 - val_accuracy: 1.0000\n",
            "Epoch 96/120\n",
            "5/5 [==============================] - 0s 10ms/step - loss: 0.0024 - accuracy: 1.0000 - val_loss: 0.0021 - val_accuracy: 1.0000\n",
            "Epoch 97/120\n",
            "5/5 [==============================] - 0s 11ms/step - loss: 0.0024 - accuracy: 1.0000 - val_loss: 0.0021 - val_accuracy: 1.0000\n",
            "Epoch 98/120\n",
            "5/5 [==============================] - 0s 11ms/step - loss: 0.0023 - accuracy: 1.0000 - val_loss: 0.0020 - val_accuracy: 1.0000\n",
            "Epoch 99/120\n",
            "5/5 [==============================] - 0s 11ms/step - loss: 0.0023 - accuracy: 1.0000 - val_loss: 0.0020 - val_accuracy: 1.0000\n",
            "Epoch 100/120\n",
            "5/5 [==============================] - 0s 10ms/step - loss: 0.0022 - accuracy: 1.0000 - val_loss: 0.0019 - val_accuracy: 1.0000\n",
            "Epoch 101/120\n",
            "5/5 [==============================] - 0s 11ms/step - loss: 0.0021 - accuracy: 1.0000 - val_loss: 0.0019 - val_accuracy: 1.0000\n",
            "Epoch 102/120\n",
            "5/5 [==============================] - 0s 10ms/step - loss: 0.0021 - accuracy: 1.0000 - val_loss: 0.0018 - val_accuracy: 1.0000\n",
            "Epoch 103/120\n",
            "5/5 [==============================] - 0s 14ms/step - loss: 0.0020 - accuracy: 1.0000 - val_loss: 0.0018 - val_accuracy: 1.0000\n",
            "Epoch 104/120\n",
            "5/5 [==============================] - 0s 16ms/step - loss: 0.0020 - accuracy: 1.0000 - val_loss: 0.0017 - val_accuracy: 1.0000\n",
            "Epoch 105/120\n",
            "5/5 [==============================] - 0s 15ms/step - loss: 0.0020 - accuracy: 1.0000 - val_loss: 0.0017 - val_accuracy: 1.0000\n",
            "Epoch 106/120\n",
            "5/5 [==============================] - 0s 10ms/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 0.0017 - val_accuracy: 1.0000\n",
            "Epoch 107/120\n",
            "5/5 [==============================] - 0s 12ms/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 0.0016 - val_accuracy: 1.0000\n",
            "Epoch 108/120\n",
            "5/5 [==============================] - 0s 9ms/step - loss: 0.0018 - accuracy: 1.0000 - val_loss: 0.0016 - val_accuracy: 1.0000\n",
            "Epoch 109/120\n",
            "5/5 [==============================] - 0s 10ms/step - loss: 0.0018 - accuracy: 1.0000 - val_loss: 0.0016 - val_accuracy: 1.0000\n",
            "Epoch 110/120\n",
            "5/5 [==============================] - 0s 15ms/step - loss: 0.0017 - accuracy: 1.0000 - val_loss: 0.0015 - val_accuracy: 1.0000\n",
            "Epoch 111/120\n",
            "5/5 [==============================] - 0s 11ms/step - loss: 0.0017 - accuracy: 1.0000 - val_loss: 0.0015 - val_accuracy: 1.0000\n",
            "Epoch 112/120\n",
            "5/5 [==============================] - 0s 18ms/step - loss: 0.0017 - accuracy: 1.0000 - val_loss: 0.0015 - val_accuracy: 1.0000\n",
            "Epoch 113/120\n",
            "5/5 [==============================] - 0s 11ms/step - loss: 0.0016 - accuracy: 1.0000 - val_loss: 0.0015 - val_accuracy: 1.0000\n",
            "Epoch 114/120\n",
            "5/5 [==============================] - 0s 16ms/step - loss: 0.0016 - accuracy: 1.0000 - val_loss: 0.0014 - val_accuracy: 1.0000\n",
            "Epoch 115/120\n",
            "5/5 [==============================] - 0s 11ms/step - loss: 0.0016 - accuracy: 1.0000 - val_loss: 0.0014 - val_accuracy: 1.0000\n",
            "Epoch 116/120\n",
            "5/5 [==============================] - 0s 14ms/step - loss: 0.0015 - accuracy: 1.0000 - val_loss: 0.0014 - val_accuracy: 1.0000\n",
            "Epoch 117/120\n",
            "5/5 [==============================] - 0s 15ms/step - loss: 0.0015 - accuracy: 1.0000 - val_loss: 0.0013 - val_accuracy: 1.0000\n",
            "Epoch 118/120\n",
            "5/5 [==============================] - 0s 15ms/step - loss: 0.0015 - accuracy: 1.0000 - val_loss: 0.0013 - val_accuracy: 1.0000\n",
            "Epoch 119/120\n",
            "5/5 [==============================] - 0s 11ms/step - loss: 0.0014 - accuracy: 1.0000 - val_loss: 0.0013 - val_accuracy: 1.0000\n",
            "Epoch 120/120\n",
            "5/5 [==============================] - 0s 10ms/step - loss: 0.0014 - accuracy: 1.0000 - val_loss: 0.0013 - val_accuracy: 1.0000\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.src.callbacks.History at 0x7b922f189150>"
            ]
          },
          "metadata": {},
          "execution_count": 158
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "loss, accuracy = model.evaluate(X_test, y_test)\n",
        "print(f\"Test accuracy: {accuracy:.2f}\")\n"
      ],
      "metadata": {
        "id": "WrOIbGFtp5td",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "41f0759f-e821-49a0-eaac-54c60d8c6cf9"
      },
      "execution_count": 157,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2/2 [==============================] - 0s 8ms/step - loss: 0.1661 - accuracy: 0.9500\n",
            "Test accuracy: 0.95\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "y_pred = model.predict(X_test).round()\n",
        "cm = confusion_matrix(y_test, y_pred)\n",
        "disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=['No Eve', 'With Eve'])\n",
        "disp.plot(cmap=plt.cm.Blues)\n",
        "plt.show()\n"
      ],
      "metadata": {
        "id": "Us7IRUjIp9nQ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "outputId": "de5ba4e4-8a30-4de6-c01c-8ca4ef92dac5"
      },
      "execution_count": 150,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2/2 [==============================] - 0s 6ms/step\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score\n",
        "\n",
        "\n",
        "accuracy = accuracy_score(y_test, y_pred)\n",
        "precision = precision_score(y_test, y_pred)\n",
        "recall = recall_score(y_test, y_pred)\n",
        "f1 = f1_score(y_test, y_pred)\n",
        "\n",
        "tn, fp, fn, tp = confusion_matrix(y_test, y_pred).ravel()\n",
        "specificity = tn / (tn + fp)\n",
        "\n",
        "print(f'Accuracy: {accuracy:.4f}')\n",
        "print(f'Precision: {precision:.4f}')\n",
        "print(f'Recall (Sensitivity): {recall:.4f}')\n",
        "print(f'F1 Score: {f1:.4f}')\n",
        "print(f'Specificity (True Negative Rate): {specificity:.4f}')\n"
      ],
      "metadata": {
        "id": "k5o42E4mp_tm",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ba7cbb68-d72f-4c97-8b45-19fd872daa92"
      },
      "execution_count": 151,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.9500\n",
            "Precision: 0.9474\n",
            "Recall (Sensitivity): 0.9474\n",
            "F1 Score: 0.9474\n",
            "Specificity (True Negative Rate): 0.9524\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Xbwaes4lOS-C"
      },
      "execution_count": 58,
      "outputs": []
    }
  ]
}