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It utilizes machine learning models, network monitoring tools, and intrusion detection systems to monitor and respond to security threats in real-time.\n\n## Features\n- Network anomaly detection using machine learning models\n- Integration with Snort and Zeek for intrusion detection\n- Automated response mechanisms for handling security threats\n- Real-time monitoring and logging of network \n\n## Usage\n1. Initialize the cybersecurity system:\n   ```bash\n   python atu.py\n   ```\n2. Monitor network traffic and detect anomalies:\n   - The system will start detecting intrusions based on alerts and logs from Snort and Zeek.\n   - Anomalies in network traffic will be detected using machine learning models.\n\n## Configuration\n- Modify the `config.py` file to customize settings such as model parameters and response actions.\n- Ensure that the `send_alert()` function is configured to handle alerts appropriately.\n\n## Contributing\nContributions are welcome! If you'd like to contribute to this project, please follow these steps:\n1. Fork the repository\n2. Create a new branch (`git checkout -b feature`)\n3. Make your changes\n4. Commit your changes (`git commit -am 'Add new feature'`)\n5. Push to the branch (`git push origin feature`)\n6. Create a new Pull Request\n\n## License\nThis project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.\n\n## Acknowledgements\n- [Scapy](https://scapy.net/) - For network packet manipulation\n- [Snort](https://www.snort.org/) - For intrusion detection\n- [Zeek](https://www.zeek.org/) - For network security \n","funding_links":[],"categories":[],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Faigptcode%2Fanalysis-tcp-udp","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Faigptcode%2Fanalysis-tcp-udp","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Faigptcode%2Fanalysis-tcp-udp/lists"}