DAIR BoosterPack: AI-Powered Vulnerability Scanning and Mitigation on Amazon Linux

Project Description

The DAIR BoosterPack is an advanced cybersecurity project developed by CANARIE. It leverages AI-powered vulnerability scanning and mitigation techniques to secure AWS Amazon-Linux-based environments. This project integrates Docker, Ansible, and Trivy tools to automate vulnerability detection and mitigation with OpenSearch tools for data ingestion, searching, visualization, and analysis.

Key Features

1. Automated Vulnerability Scanning:
   • Leverage AI to prioritize vulnerabilities based on exploitability and potential impact.
   • Use tools like OpenSCAP, Lynis, ClamAV and Trivy for baseline scans.
   • Integrate AI models (e.g., TensorFlow, PyTorch) to identify unusual patterns and unknown vulnerabilities.
2. Real-Time Threat Detection:
   • Implement machine learning algorithms to analyze system logs and network traffic.
   • Use anomaly detection to flag potential zero-day vulnerabilities.
3. Patch Management:
   • Patches are automatically applied based on the severity of the vulnerability.
   • Use AWS Systems Manager Patch Manager for automated patching.
4. Mitigation Strategies:
   • Provide automated remediation for common vulnerabilities (e.g., misconfigurations, outdated software).
   • Integrate with AWS Lambda to trigger custom mitigation scripts.
5. Reporting and Alerting:
   • Generate detailed vulnerability reports and trend analysis.
   • Set up alerting via AWS SNS (Simple Notification Service) for real-time notifications.
6. CI/CD Integration:
   • Integrate with Jenkins or GitHub Actions to automate the scanning process as part of the deployment pipeline.
   • Ensure that vulnerabilities are identified and mitigated before the code reaches production.

Project Structure

ai-vulnerability-scanner/
│
├── config/
│   ├── openscap.xml          # OpenSCAP configuration files
│   ├── lynis.conf            # Lynis configuration files
│   └── ClamAV.conf           # ClamAV configuration files
│
├── models/
│   ├── anomaly_detection.py  # Anomaly detection ML model
│   └── vuln_classification.py # Vulnerability classification model
│
├── scripts/
│   ├── scan.sh               # Shell script to initiate scans
│   ├── patch_management.sh   # Script to manage patches
│   └── mitigation.py         # Python script for automated mitigation
│
├── lambda/
│   ├── trigger_lambda.py     # Lambda function to trigger scripts
│
├── ci-cd/
│   ├── jenkinsfile           # Jenkins pipeline configuration
│   └── github-actions.yml    # GitHub Actions workflow
│
├── reports/
│   ├── latest_report.html    # Latest vulnerability report
│
└── README.md                 # Project documentation

Getting Started

1. Prerequisites:
   • AWS Account: Necessary for setting up the Amazon-Linux-based environment.
   • Python 3.x, TensorFlow/PyTorch, Boto3.
   • Tools: OpenSCAP, Lynis, ClamAV
   • Docker: To run containerized services for scanning and mitigation.
   • Ansible: Required for automation of deployment and configuration tasks.
   • OpenSearch: For ingesting, searching, and visualizing the vulnerability data.
2. Installation:
   • Set up the AWS Amazon-Linux Environment: Configure an AWS EC2 instance with Amazon-Linux.
   • Clone the repository: git clone https://github.com/yourusername/ai-vulnerability-scanner.git
   • Install dependencies: pip install -r requirements.txt
   • Install Docker: Follow Docker's installation guide for your instance.
   • Install Ansible: Use the package manager to install Ansible on your instance.
   • Deploy Trivy: Install Trivy within Docker to begin scanning containers for vulnerabilities.
   • Configure OpenSearch: Set up OpenSearch for data ingestion and visualization.
3. Usage:
   • Run the initial vulnerability scan: bash scripts/scan.sh
   • Trigger AI-powered analysis: python models/anomaly_detection.py
   • Apply patches: bash scripts/patch_management.sh
   • View reports: Open reports/latest_report.html in your browser.

Future Enhancements

• AI Model Improvements: Enhance the accuracy of the AI models by incorporating more training data.
• Docker Integration: Containerize the solution for easier deployment across environments.
• Support for Other Linux Distributions: Extend support to Ubuntu, CentOS, and others.
• Enhanced Reporting: Develop a web interface for real-time monitoring and reporting.

Contribution by CANARIE

CANARIE played a pivotal role in supporting the development and deployment of the DAIR BoosterPack. Through their resources and expertise, CANARIE facilitated the integration of advanced AI-driven techniques, ensuring that the project aligns with industry standards for cloud security. Their contributions have been instrumental in making this project a valuable tool for cybersecurity professionals looking to enhance their cloud security practices.