A modern web-based cybersecurity tool with AI-powered network attack detection and visualization.
- Real-time network traffic monitoring and analysis
- Advanced AI-powered attack detection using RandomForest and GradientBoosting
- Anomaly detection for potential zero-day attacks
- Detection of 20 different network-based attack types
- Comprehensive packet analysis and visualization
- Modern, responsive web interface with Bootstrap 5
- Interactive dashboard with real-time visualizations using Chart.js
- Dark mode support with persistent settings
- Customizable monitoring and detection settings
- Real-time alerts and notifications
- Supported Attack Types:
- DDoS (Distributed Denial of Service)
- DoS (Denial of Service)
- Port Scanning
- SYN Flood
- UDP Flood
- Ping Flood (ICMP Flood)
- TCP Reset Attack
- ARP Spoofing
- DNS Spoofing
- IP Spoofing
- MAC Spoofing
- DHCP Spoofing
- Session Hijacking
- Replay Attack
- Smurf Attack
- ICMP Redirect Attack
- Deauth Attack
- NetBIOS Enumeration
- Network Worm
- Man-in-the-Middle
- RandomForest and GradientBoosting classifiers
- Feature selection and hyperparameter tuning
- Advanced feature engineering with 24 network traffic features
- Anomaly detection for unknown attack patterns
- Flask-based web application (backend)
- RESTful API for monitoring and statistics
- Bootstrap 5 and Chart.js (frontend)
- Persistent storage for attack logs and detection history
- Python 3.8+
- Flask and Flask-CORS
- scikit-learn 1.0+
- numpy
- joblib
- Modern web browser with JavaScript enabled
# Clone the repository
git clone https://github.com/root0emir/antiNETattack.git
cd antiNETattack
# Install dependencies
pip install -r requirements.txt
# Run the application
python app.pyThen open your browser to http://localhost:5000
python app.py
- Access the web interface at http://localhost:5000
- Start the network monitoring
- View real-time attack detection results
MIT License