A comprehensive cybersecurity platform that combines anomaly detection, steganography analysis, and automated threat monitoring with intelligent alerting systems.
🎥 Demo videos available in /Other Resources/individual workings/
- Main Demo: ThreatPeek.mov
- Feature Demos: Basic scan.mp4, deepscan.mp4, reposcan.mp4, sqli.mp4, xss.mp4, etc.
ThreatPeek is a multi-component cybersecurity solution designed to detect, analyze, and respond to various security threats. The platform integrates machine learning-based anomaly detection with steganography analysis and provides automated alerting through multiple channels.
The platform consists of several interconnected components:
ThreatPeek/
├── 🤖 AnomolyDetection/ # ML-based anomaly detection system
├── ⚡ Automation/ # n8n workflow automation for alerts
├── 🖥️ Backend/ # Node.js/Express API server
├── 🌐 Frontend/ # Next.js React web application
├── 🔐 StegnoShield/ # Steganography analysis service
├── 🧩 StegnoShield Extension/ # Browser extension for image analysis
├── 📁 Resources/ # Additional steganography services
├── 🧪 sandbox/ # Development and testing environment
└── 📚 Other Resources/ # Documentation and demo videos
- Machine Learning Anomaly Detection: Advanced ML models using Random Forest and Isolation Forest algorithms
- Steganography Analysis: Detect hidden content in images using specialized algorithms
- Multi-vector Scanning: Support for XSS, SQL injection, and repository scanning
- Real-time Monitoring: Continuous threat assessment and monitoring
- Multi-channel Notifications: Email, WhatsApp, and SMS alerts via automated workflows
- Severity-based Routing: Intelligent alert routing based on threat severity and confidence levels
- Integration Support: Google Sheets logging and webhook-based integrations
- Customizable Workflows: n8n-powered automation for flexible alert management
- Modern Web Dashboard: Next.js-based responsive web application
- Browser Extension: Chrome extension for on-the-fly image analysis
- Real-time Updates: Live threat status and monitoring dashboards
- Mobile-responsive Design: Optimized for desktop and mobile devices
- RESTful APIs: Well-documented API endpoints for integration
- Microservices Architecture: Scalable and maintainable component design
- Docker Support: Containerized deployment options
- Comprehensive Logging: Detailed audit trails and monitoring
- Node.js 18+ and npm
- Python 3.8+ (for ML components)
- Git
-
Clone the repository
git clone <repository-url> cd ThreatPeek-Project
-
Install Backend Dependencies
cd Backend npm install -
Install Frontend Dependencies
cd ../Frontend npm install -
Setup StegnoShield Service
cd ../StegnoShield/stegoshield_service pip install -r requirements.txt -
Install Browser Extension Dependencies
cd "../../StegnoShield Extension" npm install
-
Start the Backend Server
cd Backend npm start # Server runs on http://localhost:3000
-
Start the Frontend Application
cd Frontend npm run dev # Web app runs on http://localhost:3001
-
Start StegnoShield Service
cd StegnoShield/stegoshield_service uvicorn main:app --reload # Service runs on http://localhost:8000
-
Build Browser Extension
cd "StegnoShield Extension" npm run build # Extension built in build/ directory
- Technology: Python, Jupyter Notebooks, Scikit-learn
- Models: Random Forest, Isolation Forest
- Features: Data preprocessing, feature engineering, model evaluation
- Output: Threat confidence scores and anomaly classifications
- Platform: n8n workflow automation
- Integrations: Gmail, Twilio, Google Sheets, Webhooks
- Features: Conditional routing, template-based messaging, data logging
- Triggers: Real-time anomaly events and scheduled checks
- Technology: Node.js, Express.js
- Features: RESTful endpoints, middleware security, request validation
- Integrations: ML model APIs, database connections, external services
- Security: Helmet.js, CORS, input sanitization
- Technology: Next.js, React, Radix UI
- Features: Real-time dashboards, responsive design, dark/light themes
- Components: Charts, alerts, user management, settings
- Deployment: Static generation, serverless functions
- Technology: Python, FastAPI, Computer Vision
- Features: Image analysis, steganography detection, metadata extraction
- API: RESTful endpoints for image processing
- Integration: Browser extension and web dashboard support
Each component maintains its own dependencies and can be developed independently:
- Backend: Express.js server with security middleware
- Frontend: Next.js with modern React patterns
- ML Services: Python-based microservices with FastAPI
- Browser Extension: Plasmo-based Chrome extension
- Automation: n8n workflow definitions
Refer to individual component READMEs for detailed setup instructions:
The StegnoShield browser extension provides on-the-fly image analysis capabilities:
- Technology: Plasmo framework, React, TypeScript
- Features: Right-click context menu, popup interface, background processing
- Analysis: Real-time steganography detection in web images
- Integration: Seamless connection with backend services
- Input Validation: Comprehensive request sanitization
- CORS Protection: Configured cross-origin policies
- Helmet Integration: Security headers and protections
- Rate Limiting: API endpoint protection
- Secure Communications: HTTPS/WSS protocols
- Data Encryption: Sensitive data protection
- Real-time Dashboards: Live threat status monitoring
- Historical Analysis: Trend analysis and reporting
- Alert Management: Centralized notification handling
- Performance Metrics: System health and usage statistics
- Audit Trails: Comprehensive activity logging
We welcome contributions to ThreatPeek! Please see our Contributing Guidelines for details on:
- Code style and standards
- Pull request process
- Issue reporting
- Development workflow
This project is licensed under the MIT License - see the LICENSE file for details.
- Documentation: Check component-specific READMEs for detailed guides
- Issues: Report bugs and feature requests via GitHub Issues
- Security: Report security vulnerabilities via our Security Policy
ThreatPeek is proudly developed by a collaborative team of four passionate developers:
- Jayesh RL - Team Lead(FullStack and integration) (GitHub)
- Rajath U - Ml (GitHub)
- Vaishanth Mohan - UI/UX and Agentic Automation (GitHub)
- Sinchana Benakatti - CyberSec (GitHub)
For detailed information about each contributor's role and contributions, see our CONTRIBUTORS.md file.
- Open source libraries and frameworks used
- Security research community contributions
- Machine learning model training datasets
- Testing and feedback from security professionals
- Our amazing team collaboration and shared vision
ThreatPeek - Comprehensive cybersecurity through intelligent detection and automated response
For detailed setup and usage instructions, please refer to the individual component documentation in their respective directories.