This project implements a cybersecurity system for detecting anomalies and intrusions in network traffic. It utilizes machine learning models, network monitoring tools, and intrusion detection systems to monitor and respond to security threats in real-time.

• Network anomaly detection using machine learning models
• Integration with Snort and Zeek for intrusion detection
• Automated response mechanisms for handling security threats
• Real-time monitoring and logging of network

1. Initialize the cybersecurity system:

   python atu.py

2. Monitor network traffic and detect anomalies:
   • The system will start detecting intrusions based on alerts and logs from Snort and Zeek.
   • Anomalies in network traffic will be detected using machine learning models.

• Modify the config.py file to customize settings such as model parameters and response actions.
• Ensure that the send_alert() function is configured to handle alerts appropriately.

Contributions are welcome! If you'd like to contribute to this project, please follow these steps:

1. Fork the repository
2. Create a new branch (git checkout -b feature)
3. Make your changes
4. Commit your changes (git commit -am 'Add new feature')
5. Push to the branch (git push origin feature)
6. Create a new Pull Request

This project is licensed under the MIT License. See the LICENSE file for details.

• Scapy - For network packet manipulation
• Snort - For intrusion detection
• Zeek - For network security