Network Security Threat Detection System

An end-to-end machine learning pipeline for detecting network security threats and phishing attempts with real-time prediction capabilities.

🚀 Features

• Real-time Threat Detection: Machine learning models for network security threat prediction
• High-Performance API: FastAPI-based REST API for serving predictions
• Data Management: MongoDB integration for efficient data storage and retrieval
• Experiment Tracking: MLflow integration for model lifecycle management
• Version Control: DagsHub for collaborative ML workflows
• Robust Architecture: Comprehensive logging and exception handling
• Production Ready: Scalable deployment configuration

📋 Prerequisites

• Python 3.8+
• MongoDB database
• Git
• DagsHub account (for version control)

🛠️ Installation

1. Clone the repository

   git clone https://github.com/Suraj-G-Rao/Network_Security.git
   cd Network_Security

2. Create and activate virtual environment

   python -m venv venv
   # Windows
   venv\Scripts\activate
   # Linux/Mac
   source venv/bin/activate

3. Install dependencies

   pip install -r requirements.txt

4. Set up environment variables

   # Create a .env file in the root directory
   # Add your MongoDB connection string
   MONGODB_URL_KEY=your_mongodb_connection_string

5. Install the package

   pip install -e .

🏗️ Project Structure

Network_Security/
├── src/                    # Source code
│   ├── components/         # ML pipeline components
│   ├── constant/          # Configuration constants
│   ├── entity/            # Data entities and configs
│   ├── exception/         # Custom exceptions
│   ├── logging/           # Logging utilities
│   ├── pipeline/          # ML pipelines
│   └── utils/             # Utility functions
├── app.py                 # FastAPI application
├── main.py               # Training pipeline entry point
├── requirements.txt      # Python dependencies
├── setup.py             # Package configuration
├── Network_data/        # Training data
├── final_model/         # Trained models
└── templates/           # HTML templates

🚀 Usage

Training the Model

Run the complete training pipeline:

python main.py

This will execute:

1. Data Ingestion: Load and prepare training data
2. Data Validation: Validate data quality and schema
3. Data Transformation: Preprocess and feature engineer
4. Model Training: Train the network security model
5. Model Evaluation: Evaluate model performance

Running the API

Start the FastAPI server:

python app.py

Or using uvicorn directly:

uvicorn app:app --host 0.0.0.0 --port 8000 --reload

The API will be available at http://localhost:8000

API Endpoints

• GET /: Root endpoint - redirects to prediction interface
• GET /docs: Interactive API documentation (Swagger UI)
• POST /predict: Make predictions on network data

Making Predictions

Send a POST request to /predict with network data:

curl -X POST "http://localhost:8000/predict" \
     -H "Content-Type: application/json" \
     -d '{"features": [your_network_features_here]}'

🔧 Configuration

MongoDB Setup

1. Set up a MongoDB cluster (MongoDB Atlas recommended)
2. Create a database and collection for network data
3. Add the connection string to your .env file

DagsHub Integration

1. Create a DagsHub repository
2. Configure MLflow tracking:

   import mlflow
   mlflow.set_tracking_uri("your_dagshub_mlflow_uri")

📊 Model Performance

The system includes comprehensive model evaluation with:

• Accuracy metrics
• Precision and recall scores
• Confusion matrix visualization
• Feature importance analysis

🛡️ Security Features

• Input Validation: Comprehensive data validation pipeline
• Error Handling: Robust exception handling throughout
• Logging: Detailed logging for monitoring and debugging
• CORS Configuration: Secure cross-origin resource sharing

🤝 Contributing

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

📝 License

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

👨‍💻 Author

Suraj G Rao

• Email: surajgrao0203@gmail.com
• GitHub: Suraj-G-Rao

🙏 Acknowledgments

• MLflow for experiment tracking
• FastAPI for high-performance API framework
• MongoDB for data storage
• Scikit-learn for machine learning algorithms
• DagsHub for version control and collaboration

📞 Support

For any queries or support, please reach out via:

• Email: surajgrao0203@gmail.com
• GitHub Issues: Create an issue