This advanced AI-based Intrusion Detection System (IDS) includes:
Key Features:
1. Multiple AI Models:
- Random Forest Classifier
- XGBoost Classifier
- Neural Network (MLP)
- One-Class SVM for anomaly detection
- Ensemble voting system
2. Real-time Monitoring:
- Live network traffic visualization
- Packet capture and analysis
- Real-time threat detection
3. GUI Components:
- Real-time Monitor: Live traffic graphs
- Alert Panel: Color-coded intrusion alerts
- Packet Analyzer: Detailed packet inspection
- AI Models Panel: Model performance metrics
- System Statistics: Live metrics display
4. Advanced Features:
- Threat Level Calculation: Dynamic threat assessment
- Automated Response: Simulated blocking of threats
- Attack Simulation: Test various attack scenarios
- Model Training Interface: On-demand AI training
5. Detection Capabilities:
- Port scanning
- DDoS attacks
- SQL injection patterns
- XSS attempts
- Brute force attacks
- Malware command & control traffic
- Anomaly detection
Installation Requirements:
Create a requirements.txt file:
txt
numpy=1.21.0
pandas=1.3.0
scikit-learn=1.0.0
xgboost=1.5.0
tensorflow=2.8.0
matplotlib=3.5.0
seaborn=0.11.0
scapy=2.4.5
Install with:
bash
pip install -r requirements.txt
Usage:
- Start the System: Run the Python script
- Train Models: Click "Train Models" to initialize AI
- Start Monitoring: Begin real-time network analysis
- View Alerts: Monitor the alert panel for intrusions
- Analyze Packets: Inspect individual packets in the analyzer
- Simulate Attacks: Test the system with simulated threats
Security Features:
- Multi-model ensemble for improved accuracy
- Real-time anomaly detection
- Automatic threat blocking (simulated)
- Comprehensive logging of all events
- Visual threat indicators
Extensibility:
The system is designed to be extended with:
- Additional ML models
- Custom detection rules
- Integration with firewalls
- Cloud-based threat intelligence
- Mobile alerts
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