🎯 Centralized Vulnerability Detection & Intelligent Query Interface

Overview

An integrated AI-powered cybersecurity platform that consolidates vulnerability scanning tools (Nmap, OpenVAS, Nessus, Nikto, Nuclei) with intelligent threat analysis using Google Gemini and LangChain RAG.

🚀 Features

Phase 1: Vulnerability Scanning

• Multi-tool Integration: Nmap, OpenVAS, Nessus, Nikto, Nuclei
• Responsive Web GUI: Modern dashboard for scan management
• Real-time Progress: Live scanning status and updates

Phase 2: Intelligence & Correlation

• CVE Correlation: Automatic matching with NVD database
• CVSS Scoring: Risk prioritization and impact assessment
• Attack Path Generation: MITRE ATT&CK framework integration
• Threat Intelligence: ExploitDB, Rapid7, CISA KEV integration

Phase 3: AI-Powered Analysis

• RAG Chatbot: Google Gemini with LangChain implementation
• Natural Language Queries: Ask questions about vulnerabilities
• Contextual Responses: Exploit steps and remediation guidance
• Multi-user Support: Concurrent chatbot interactions

🛠 Technology Stack

• Backend: Python Flask, SQLAlchemy, PostgreSQL
• AI/ML: LangChain, Google Gemini, ChromaDB, FAISS
• Frontend: HTML5, CSS3, JavaScript (Vanilla)
• Scanners: Nmap, OpenVAS, Nessus, Nikto, Nuclei
• Containerization: Docker, Docker Compose
• Task Queue: Celery with Redis

📋 Prerequisites

• Python 3.9+
• Docker & Docker Compose
• PostgreSQL 13+
• Redis Server
• Google API Key (for Gemini)

System Requirements

• RAM: 8GB minimum, 16GB recommended
• Storage: 20GB for vulnerability databases
• Network: Internet access for threat intelligence feeds

🚀 Quick Start

1. Clone & Setup

git clone <repository-url>
cd centralized-vulnerability-detection
cp .env.example .env

2. Configure Environment

Edit .env file:

GOOGLE_API_KEY=your_gemini_api_key_here
DATABASE_URL=postgresql://user:pass@localhost:5432/vuln_db
REDIS_URL=redis://localhost:6379/0

3. Install Dependencies

pip install -r requirements.txt

4. Database Setup

python -m flask db init
python -m flask db migrate
python -m flask db upgrade

5. Start Services

# Using Docker Compose (Recommended)
docker-compose up -d

# Or manually
python app/main.py

6. Access Application

• Web Interface: http://localhost:5000
• API Documentation: http://localhost:5000/docs
• Admin Panel: http://localhost:5000/admin

📖 Usage Guide

Starting a Vulnerability Scan

1. Navigate to the dashboard
2. Enter target (IP, domain, or network range)
3. Select scanning tools (Nmap, OpenVAS, etc.)
4. Click "Start Scan"
5. Monitor progress in real-time

Using the AI Chatbot

1. Go to the Chat interface
2. Ask questions like:
   • "What are the critical vulnerabilities?"
   • "How do I exploit CVE-2023-12345?"
   • "Show me attack paths for high-risk hosts"
3. Get intelligent responses with references

Generating Reports

1. Select completed scans
2. Choose report format (PDF, JSON, XML)
3. Download structured vulnerability reports

🏗 Architecture

┌─────────────────┐    ┌──────────────────┐    ┌─────────────────┐
│   Web Frontend  │◄──►│  Flask Backend   │◄──►│   PostgreSQL    │
└─────────────────┘    └──────────────────┘    └─────────────────┘
                               │
                               ▼
                    ┌──────────────────┐
                    │  Scanner Tools   │
                    │ Nmap│OpenVAS│... │
                    └──────────────────┘
                               │
                               ▼
                    ┌──────────────────┐
                    │   RAG System     │
                    │ Gemini│LangChain │
                    └──────────────────┘

🔧 Configuration

Scanner Tools Setup

• Nmap: Pre-installed with python-nmap
• OpenVAS: Configure in scanners/openvas_client.py
• Nessus: Add API keys in environment variables
• Nikto: Command-line integration
• Nuclei: Template-based scanning

AI Configuration

• Google Gemini: Set GOOGLE_API_KEY in .env
• Vector Database: ChromaDB for embeddings
• Knowledge Base: Automatic threat intelligence updates

🧪 Testing

# Run all tests
pytest

# Test specific components
pytest tests/test_scanners.py
pytest tests/test_rag.py
pytest tests/test_api.py

# Coverage report
pytest --cov=app tests/

📊 API Endpoints

Scanning APIs

• POST /api/scans/start - Start vulnerability scan
• GET /api/scans/{id}/status - Check scan progress
• GET /api/scans/{id}/results - Get scan results

Vulnerability APIs

• GET /api/vulnerabilities - List all vulnerabilities
• GET /api/vulnerabilities/{id} - Get vulnerability details
• POST /api/vulnerabilities/search - Search vulnerabilities

Chat APIs

• POST /api/chat/query - Send query to AI chatbot
• GET /api/chat/history - Get chat history
• DELETE /api/chat/clear - Clear chat session

🔒 Security Considerations

• Data Encryption: All sensitive data encrypted at rest
• API Authentication: JWT-based authentication system
• Input Validation: Comprehensive input sanitization
• Rate Limiting: API rate limiting implemented
• Audit Logging: Complete audit trail for all actions

📈 Performance Optimization

• Async Processing: Celery for background tasks
• Database Indexing: Optimized queries for large datasets
• Caching: Redis caching for frequent queries
• Connection Pooling: Database connection optimization

🚀 Deployment

Production Deployment

# Build production image
docker build -t vuln-detector .

# Deploy with scaling
docker-compose -f docker-compose.prod.yml up -d --scale worker=3

Environment-specific Configs

• Development: config/dev.py
• Testing: config/test.py
• Production: config/prod.py

🤝 Contributing

1. Fork the repository
2. Create feature branch (git checkout -b feature/AmazingFeature)
3. Commit changes (git commit -m 'Add AmazingFeature')
4. Push to branch (git push origin feature/AmazingFeature)
5. Open Pull Request

📝 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙋‍♂️ Support

• Documentation: /docs folder
• Issues: GitHub Issues tracker
• Email: support@vulndetector.com

🗺 Roadmap

v1.0 (Current)

• ✅ Basic vulnerability scanning
• ✅ Google Gemini RAG integration
• ✅ Web dashboard

v1.1 (Next Release)

• 🔄 Advanced attack path visualization
• 🔄 Compliance reporting (SOX, HIPAA, PCI-DSS)
• 🔄 Mobile application

v2.0 (Future)

• 📋 Machine learning for false positive reduction
• 📋 Integration with SIEM/SOAR platforms
• 📋 Enterprise SSO support

---

Built with ❤️ for SIH 2025 Hackathon