DDoS Attack Detection System
A real-time DDoS attack detection system designed for enterprise-grade banking security. This system leverages multiple machine learning models and live network monitoring to identify, score, and mitigate malicious traffic efficiently.
Features
ML-Powered Detection Engine: Combines LSTM, CNN, Random Forest, and SVM models for high-accuracy threat classification.
Live Network Monitoring: Continuous packet capture, anomaly scoring, and traffic heatmaps.
Threat Intelligence Integration: Uses public API feeds and geolocation mapping for context-aware alerts.
Real-Time Alerts: Automated responses via Email, SMS, and Slack.
Interactive Dashboard: Visual analytics for attack vectors, bandwidth spikes, and regional threat origins.
Tech Stack
Frontend: Next.js
Backend: Supabase
Machine Learning: Python (LSTM, CNN, Random Forest, SVM)
Real-Time Data: WebSockets
Visualization: Chart.js
APIs: REST API integration
Setup Instructions
Clone the repository
git clone https://github.com/Avnee29/DDoS-Detection.git cd DDoS-Detection
Install dependencies Follow instructions for your frontend/backend environment (Next.js & Supabase setup).
Run the application
npm run dev
npm run dev
Access the dashboard in your browser at http://localhost:3000.
Description
This system processes thousands of packets per second, automatically detects malicious traffic, and triggers immediate mitigation. It is designed to provide robust protection against modern DDoS threats while offering detailed visual insights for network administrators.
License
This project is licensed under the MIT License.