A comprehensive blockchain fraud detection system that combines AI/ML fraud detection capabilities with a robust full-stack application. This project provides real-time analysis of blockchain transactions to identify potentially fraudulent activities on the ICP blockchain.
The system consists of three main components:
- AI/ML Service: Python-based fraud detection module using RandomForestClassifier
- Backend API: Rust-based backend service providing RESTful APIs
- Frontend Dashboard: React-based web application for user interaction
- Database: MongoDB for data persistence
Before setting up the project, ensure you have the following installed:
- Docker (for MongoDB)
- Python 3.8+ (for AI/ML service)
- Rust 1.70+ (for backend service)
- Node.js 16+ and npm (for frontend)
- Git (for version control)
- MongoDB Compass (for database management)
- Postman or Thunder Client (for API testing)
- VS Code or your preferred IDE
git clone https://github.com/basedanurag/ICP--Blockchain-Fraud-Detector.git
cd "ICP--Blockchain-Fraud-Detector"# Start MongoDB using Docker
docker run -d -p 27017:27017 --name mongodb mongo:latest
# Verify MongoDB is running
docker ps# Navigate to fraud detection module
cd fraud_detection_module
# Create virtual environment (recommended)
python -m venv venv
# Activate virtual environment
# On Windows:
venv\Scripts\activate
# On macOS/Linux:
source venv/bin/activate
# Install Python dependencies
pip install -r requirements.txt
# Train the fraud detection model (if not already present)
python train_model.py
# Test the fraud detection module
python test_fraud_detection.py
# Return to project root
cd ..# Navigate to backend directory
cd backend
# Build the Rust project
cargo build
# Run the backend service
cargo run
# Backend will be available at http://localhost:8080
# In a new terminal, return to project root
cd ..# Navigate to frontend directory
cd frontend
# Install Node.js dependencies
npm install
# Start the development server
npm start
# Frontend will be available at http://localhost:3000Once all services are running, you can access:
- Frontend Dashboard: http://localhost:3000
- Backend API: http://localhost:8080
- MongoDB: mongodb://localhost:27017
- AI/ML Service: Integrated with backend
# Test backend health endpoint
curl http://localhost:8080/health
# Test fraud analysis endpoint
curl -X POST http://localhost:8080/api/analyze \
-H "Content-Type: application/json" \
-d '{"wallet_address": "0x1234567890abcdef1234567890abcdef12345678"}'- Open http://localhost:3000 in your browser
- Navigate through the dashboard
- Test fraud detection functionality
- Verify data visualization components
ICP--Blockchain-Fraud-Detector/
├── backend/ # Rust backend service
│ ├── src/
│ │ ├── main.rs # Application entry point
│ │ ├── routes.rs # API route definitions
│ │ ├── models.rs # Data models
│ │ ├── db.rs # Database operations
│ │ └── icp/ # ICP blockchain integration
│ ├── Cargo.toml # Rust dependencies
│ └── .env # Environment variables
├── frontend/ # React frontend application
│ ├── src/
│ ├── public/
│ ├── package.json # Node.js dependencies
│ └── tailwind.config.js # Tailwind CSS configuration
├── fraud_detection_module/ # Python AI/ML service
│ ├── fraud_detection.py # Main fraud detection logic
│ ├── train_model.py # Model training script
│ ├── test_fraud_detection.py # Testing script
│ ├── fraud_model.pkl # Trained ML model
│ └── requirements.txt # Python dependencies
├── README.md # This file
└── BACKEND_DEVELOPER_GUIDE.md # Backend development guide
cd backend
cargo check # Check for compilation errors
cargo test # Run tests
cargo run --release # Run optimized buildcd frontend
npm run build # Build for production
npm test # Run tests
npm run eject # Eject from Create React App (irreversible)cd fraud_detection_module
python train_model.py # Retrain the model
python test_fraud_detection.py # Test model performanceCreate a .env file in the backend directory:
DATABASE_URL=mongodb://localhost:27017/fraud_detection
RUST_LOG=info
SERVER_HOST=127.0.0.1
SERVER_PORT=8080The system uses MongoDB for data persistence. Ensure MongoDB is running on the default port (27017) or update the connection string in your configuration.
- Real-time Fraud Detection: AI-powered analysis of blockchain transactions
- Interactive Dashboard: React-based web interface for monitoring and analysis
- REST API: Comprehensive API for integration with external systems
- Scalable Architecture: Microservices-based design for easy scaling
- Data Visualization: Charts and graphs for fraud pattern analysis
- Historical Analysis: View historical fraud detection results
-
MongoDB Connection Error
- Ensure Docker is running:
docker ps - Restart MongoDB container:
docker restart mongodb
- Ensure Docker is running:
-
Rust Compilation Errors
- Update Rust:
rustup update - Clean build:
cargo clean && cargo build
- Update Rust:
-
Frontend Build Issues
- Clear npm cache:
npm cache clean --force - Delete node_modules:
rm -rf node_modules && npm install
- Clear npm cache:
-
Python Module Import Errors
- Ensure virtual environment is activated
- Reinstall dependencies:
pip install -r requirements.txt
- Backend logs: Console output when running
cargo run - Frontend logs: Browser console (F12)
- AI/ML logs:
fraud_detection_module/fraud_detection.log
- Fork the repository
- Create a feature branch:
git checkout -b feature/amazing-feature - Commit your changes:
git commit -m 'Add some amazing feature' - Push to the branch:
git push origin feature/amazing-feature - Open a Pull Request
- Built for DoraHacks hackathon
- Uses ICP blockchain infrastructure
- Implements machine learning for fraud detection
- Modern full-stack architecture with Rust and React
Note: This is a development setup guide. For production deployment, additional considerations for security, monitoring, and scalability should be implemented.