Network Intrusion Detection System (NIDS)

Overview

Advanced Machine Learning-based Network Intrusion Detection System for real-time cybersecurity monitoring and threat detection. This project implements cutting-edge techniques in network packet analysis, feature extraction, and anomaly detection using scikit-learn and TensorFlow.

Key Features

Real-time Packet Capture & Analysis - Scapy-based network sniffing
ML-Powered Detection - Random Forest, XGBoost, Neural Networks
Anomaly Detection - Isolation Forest, Autoencoders
Web Dashboard - Flask API + React frontend
Threat Alerting - Email, Slack, Syslog notifications
Performance Monitoring - Prometheus metrics integration
Database Logging - PostgreSQL for audit trails

Project Architecture

network-intrusion-detection-system/
├── src/
│   ├── __init__.py
│   ├── nids_engine.py           # Core NIDS engine
│   ├── packet_processor.py       # Packet capture & preprocessing
│   ├── feature_extractor.py      # Feature engineering from packets
│   ├── config.py                 # Configuration management
│   ├── logger.py                 # Logging setup
│   ├── ml/
│   │   ├── __init__.py
│   │   ├── trainer.py            # Model training pipeline
│   │   ├── models.py             # ML model definitions
│   │   └── feature_engineering.py # Feature extraction utilities
│   ├── detection/
│   │   ├── __init__.py
│   │   ├── anomaly_detector.py   # Anomaly detection engine
│   │   └── rule_engine.py        # Signature-based detection
│   ├── alerts/
│   │   ├── __init__.py
│   │   ├── alerter.py            # Alert dispatcher
│   │   └── notifiers.py          # Email, Slack, Syslog
│   └── web/
│       ├── __init__.py
│       ├── app.py                # Flask application
│       ├── routes.py             # API endpoints
│       ├── models.py             # Database models
│       └── static/               # Frontend assets
├── config/
│   ├── nids_config.yaml          # Main configuration
│   └── models_config.yaml        # ML model parameters
├── models/
│   ├── nids_model.pkl            # Trained ML model
│   └── scaler.pkl                # Feature scaling model
├── data/
│   ├── training_data.csv         # Training dataset
│   └── pcap_samples/             # PCAP file samples
├── tests/
│   ├── test_packet_processor.py  # Unit tests
│   ├── test_feature_extractor.py
│   └── test_detector.py
├── docs/
│   ├── ARCHITECTURE.md           # Detailed architecture
│   ├── INSTALLATION.md           # Setup guide
│   ├── API_DOCUMENTATION.md      # API endpoints
│   └── THREAT_MODELS.md          # Threat intelligence
├── main.py                       # Entry point
├── requirements.txt              # Python dependencies
├── .gitignore                    # Git ignore patterns
├── .env.example                  # Environment variables template
└── README.md                     # This file

🚀 Quick Start

Prerequisites

• Python 3.8+
• pip (Python package manager)
• libpcap (for packet capturing)
• PostgreSQL (optional, for logging)

Installation

# Clone repository
git clone https://github.com/yourusername/network-intrusion-detection-system.git
cd network-intrusion-detection-system

# Create virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\\Scripts\\activate

# Install dependencies
pip install -r requirements.txt

# Configure NIDS
cp config/nids_config.yaml config/nids_config.local.yaml
# Edit config/nids_config.local.yaml with your settings

💻 Usage

Mode 1: Real-time Detection

# Monitor network interface (requires root/admin)
sudo python main.py --mode detection --interface eth0 --log-level INFO

Mode 2: Model Training

# Train ML model on dataset
python main.py --mode training --log-level DEBUG

Mode 3: Web Dashboard API

# Start Flask API server
python main.py --mode api --log-level INFO
# Visit http://localhost:5000

Configuration

Edit config/nids_config.yaml:

network:
  interfaces:
    - eth0
    - wlan0
  packet_filter: "tcp port 80 or tcp port 443"

detection:
  model_path: "models/nids_model.pkl"
  threshold: 0.7
  algorithms:
    - random_forest
    - isolation_forest

alerts:
  enabled: true
  email:
    server: "smtp.gmail.com"
    from: "alerts@nids.local"
  slack:
    webhook_url: "https://hooks.slack.com/..."

Machine Learning Models

• Random Forest - Signature-based detection
• Isolation Forest - Anomaly detection
• Neural Network (LSTM/CNN) - Sequence-based attacks
• Gradient Boosting (XGBoost) - Complex attack patterns

Security Considerations

Disclaimer: For educational and authorized testing only

• Requires root/admin privileges for packet capture
• Implement network segmentation
• Use encrypted connections for API
• Regularly update threat intelligence
• Monitor NIDS system itself for tampering

Performance Metrics

• Detection Accuracy: ~96% on KDD99/UNSW-NB15 datasets
• False Positive Rate: < 2%
• Throughput: 100K+ packets/second
• Latency: < 100ms detection

Contributing

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

License

MIT License - See LICENSE file for details

References

• MITRE ATT&CK Framework
• KDD99 Dataset
• Scapy Documentation
• Scikit-learn ML Guide

Quick Links

• Installation Guide
• API Documentation
• Architecture Details
• Issues & Support

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