MLGuard is a Network Intrusion Detection System (NIDS) powered by hybrid machine learning algorithms — combining the strengths of Random Forest and XGBoost to detect and classify potential security threats in network traffic.
- 🔍 Intelligent intrusion detection using ensemble ML models
- ⚙️ Backend built with Python
- 💻 Frontend powered by React + Vite
- 📊 Real-time monitoring and analysis
From the project’s root directory, activate your virtual environment:
Scripts/activate(If you’re on Linux or macOS, the command may differ, e.g. source venv/bin/activate.)
Install all backend dependencies:
pip install -r requirements.txtNavigate to the frontend directory and start the development server:
cd frontend
npm install
npm run dev- Machine Learning: Random Forest, XGBoost
- Backend: Python
- Frontend: React (Vite)
- Environment Management: Virtualenv
Contributions, issues, and feature requests are welcome!
Feel free to submit a pull request or open an issue.
This project is released under the MIT License.