AI-Powered Security Scanner

Overview

This project combines artificial intelligence with cybersecurity to create an intelligent security scanning system that learns from historical vulnerability data and adapts to new threat patterns.

Key Features

• Real-time Threat Detection: ML models analyze code and network traffic in real-time
• Adaptive Learning: System improves accuracy by learning from new vulnerabilities
• Multi-Vector Analysis: Scans for OWASP Top 10, injection attacks, and zero-days
• API Integration: RESTful API for easy integration with CI/CD pipelines

Technical Architecture

The system is built with a microservices architecture:

1. Scanning Engine (Python + TensorFlow): Core ML models for threat detection
2. API Layer (FastAPI): High-performance API for scan requests
3. Database (PostgreSQL): Stores vulnerability signatures and scan results
4. Frontend Dashboard (React + TypeScript): Real-time monitoring interface

Machine Learning Approach

We trained custom models on a dataset of 100,000+ known vulnerabilities, using:

• Transformer models for code analysis
• CNN-based pattern recognition for binary analysis
• Ensemble methods for improved accuracy

Results

• 95% detection accuracy on known vulnerabilities
• Sub-second scan times for most codebases
• 40% reduction in false positives compared to traditional scanners

Future Enhancements

• Integration with popular IDEs (VS Code, JetBrains)
• Support for more programming languages
• Automated remediation suggestions
• Cloud-native deployment options

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Interested in contributing or learning more? Check out the GitHub repository or try the demo.