Project Overview
A network intrusion detection system that captures live network traffic, extracts features, and classifies attacks (DDoS, SQL Injection, Port Scan) using trained ML models with real-time alerting.
Objectives
- Detect network attacks in real-time
- Classify attack types accurately
- Minimize false positive rates
Features & Modules
Live network traffic monitoring
Multi-class attack classification
Real-time alert notifications
Interactive security dashboard
Attack log and history
Performance metrics visualization
Technology Stack
Frontend
ReactChart.js
Backend
PythonFlaskScapy
Database
MySQLRedis
Hardware Requirements
- Intel i7 recommended
- 16GB RAM
- Network adapter
Software Requirements
- Python 3.9+
- Wireshark
- Kali Linux (optional)
- VS Code
Future Scope
- Deep learning IDS
- Integration with SIEM systems
- Cloud-based deployment
Quick FAQs
Does it work on live networks?
Yes, the system can capture and analyze live network traffic with proper permissions.
Technologies Used
PythonScikit-learnPandasWiresharkFlask