Comprehensive security automation platform with vulnerability scanning, phishing detection, and subdomain discovery
sentinelOps is a comprehensive security automation platform providing multiple scanning and analysis tools. The platform combines a modern React frontend with a robust FastAPI backend to deliver vulnerability scanning, phishing detection, subdomain discovery, and security fix generation.
The architecture leverages machine learning models trained on security features and integrates with industry-standard security tools like pip-audit, npm audit, and osv-scanner to provide multi-language vulnerability detection across 15+ programming languages.
The platform features async/await architecture for concurrent processing, Google Gemini AI integration for intelligent fix generation, and production-ready deployments on Netlify (frontend) and Render (backend).
- CVE detection across 15+ programming languages (Python, Node.js, Go, Java, Rust, Ruby, PHP, and more)
- AI-powered security fix generation using Google Gemini
- Detailed vulnerability reports with confidence scoring
- Random Forest model trained on 30+ security features
- 95%+ accuracy in phishing detection
- Real-time URL analysis with confidence scoring
- Certificate Transparency integration
- SecurityTrails API integration
- Comprehensive subdomain discovery
- Concurrent processing with async/await architecture
- Confidence scoring for all detections
- Detailed vulnerability assessments
- Professional scan result reports
- Vulnerability assessment documentation
- Export-ready format
- Node.js 18.0 or higher
- Python 3.9 or higher
- npm or bun package manager
-
Clone the repository
git clone https://github.com/yourusername/sentinelOps.git cd sentinelOps -
Install dependencies
npm install # or bun install -
Start development server
npm run dev # or bun dev -
Build for production
npm run build
Refer to the backend repository for FastAPI setup instructions.
- React - UI framework
- TypeScript - Type safety
- Vite - Build tool
- Tailwind CSS - Styling
- Framer Motion - Animations
- Shadcn/ui - Component library
- FastAPI - Python web framework
- Python - Backend language
- Scikit-learn - Machine learning models
- PostgreSQL - Database
- Google Gemini - AI fix generation
- pip-audit - Python vulnerability scanning
- npm audit - Node.js vulnerability scanning
- osv-scanner - Multi-language vulnerability detection
- Certificate Transparency - Subdomain discovery
- SecurityTrails - Security intelligence
- User submits repository URL or code
- Tool detection based on manifest files (requirements.txt, package.json, etc.)
- Appropriate security tools execute concurrently
- ML model processes and ranks vulnerabilities
- Google Gemini generates intelligent security fixes
- Results compiled into comprehensive report
- User submits URL for analysis
- 30+ security features extracted from URL
- Random Forest classifier predicts phishing probability
- Confidence score and detailed analysis provided
- Real-time results with explanations
- Certificate Transparency logs queried
- SecurityTrails API integration for additional intelligence
- Results aggregated and deduplicated
- Comprehensive subdomain report generated
- 50+ users actively scanning repositories for security vulnerabilities
- 95%+ accuracy in phishing URL detection
- Support for 15+ programming languages
- 1000+ CVEs covered
- 50% faster vulnerability detection with async processing
- Integrated with Google Gemini AI for intelligent security fix generation
- Component-based React architecture
- TypeScript for type safety
- Responsive design with Tailwind CSS
- Smooth animations with Framer Motion
- Modular UI components from Shadcn
- Async/await with FastAPI for concurrent processing
- Machine learning models for phishing detection
- Integration with multiple security scanning tools
- Intelligent error handling and timeouts
- Production-ready with proper logging
- Frontend collects user input
- Sends request to FastAPI backend
- Backend detects tools and runs concurrent scans
- ML models process results
- AI generates intelligent fixes
- Results returned to frontend
- PDF report generation and export
Solution: Implemented tool detection based on manifest files (requirements.txt, package.json, etc.)
Solution: Used async/await architecture with aiohttp for concurrent processing and intelligent caching
Solution: Trained Random Forest Classifier with 30+ engineered security features achieving 95%+ accuracy
Solution: Built async backend with proper error handling and request timeouts
Solution: Deployed trained models using joblib with version control for reproducibility
- All external API calls validated and sanitized
- Models deployed securely with version control
- Regular vulnerability assessments
- Production deployments with environment variable protection
- HTTPS enforced for all communications
- Hosting: Vercel
- Auto-deployment: On push to main branch
- Performance: Optimized builds with Vite
- Hosting: Render
- Runtime: Python environment with dependencies
- Database: PostgreSQL managed instance
sentinelOps/
├── src/
│ ├── components/ # React components
│ ├── pages/ # Page components
│ ├── hooks/ # Custom React hooks
│ ├── lib/ # Utility functions
│ ├── api/ # API route handlers
│ └── assets/ # Static assets
├── public/
│ ├── images/ # Screenshots and images
│ ├── icons/ # Icon assets
│ └── robots.txt
├── vite.config.ts # Vite configuration
├── tsconfig.json # TypeScript configuration
├── tailwind.config.ts # Tailwind CSS configuration
└── package.json # Project dependencies
Contributions are welcome! Please follow these steps:
- Fork the repository
- Create a feature branch (
git checkout -b feature/amazing-feature) - Commit your changes (
git commit -m 'Add amazing feature') - Push to the branch (
git push origin feature/amazing-feature) - Open a Pull Request