About the Project

This project is a real-time cybersecurity visualization system that monitors global DDoS attacks. It utilizes machine learning to predict threat levels and displays these attacks on an interactive 3D globe. The system fetches malicious IP addresses from AbuseIPDB, predicts threat levels using a Random Forest ML model, and visualizes the attacks with 3D effects powered by Three.js.

Key Features:

• Real-time Threat Intelligence: Integrates with AbuseIPDB to gather live data on malicious IP addresses.
• Machine Learning Classification: Employs a Random Forest model for accurate threat level prediction, achieving 100% accuracy on the test set.
• IP Geolocation: Accurately geolocates IP addresses and utilizes caching for improved performance.
• Interactive 3D Globe Visualization: Features a dynamic 3D Earth with visual representations of attacks, including glowing spheres and pulsing beams, color-coded by threat level.
• User-Friendly Interface: Includes an information panel with live statistics, attack details on hover, and control options for fetching data and auto-refreshing.
• Deployment Ready: Supports deployment on platforms like Render.com, GitHub Pages, and Docker.

Technology Stack:

• Backend: FastAPI, scikit-learn, httpx, Pandas, NumPy, joblib, uvicorn
• Frontend: Three.js, WebGL, OrbitControls, JavaScript ES6+
• External APIs: AbuseIPDB (threat intelligence), ipinfo.io (geolocation)

This project offers a comprehensive solution for visualizing and understanding global DDoS attack patterns, combining real-time data with advanced machine learning and interactive 3D graphics.