In-Scope:
This project focuses on developing an AI-driven system to detect and respond to Advanced Persistent Threats (APTs) in real-time. The solution includes:

• Network Traffic Analysis: Utilizing machine learning models, specifically autoencoders, to identify anomalous patterns in network traffic (e.g., from pcap files).
• Generative AI Integration: Leveraging generative AI to dynamically create and implement adaptive defense strategies, such as blocking suspicious IPs, modifying firewall rules, and isolating compromised devices.
• Integration with Security Infrastructure: Seamless integration with existing security tools like SIEM systems and firewalls to enhance threat mitigation.

Out of Scope:

• Threat Intelligence Gathering: The project does not include gathering external threat intelligence or maintaining threat databases.
• User Interface Development: While the core functionality focuses on detection and response, creating a comprehensive user interface for end-users is not part of this solution.
• Post-Incident Analysis: Detailed forensic analysis of security incidents post-detection is outside the project's scope.

Future Opportunities:

• Enhanced Learning Capabilities: Implementing reinforcement learning for continuous improvement of threat detection and response strategies.
• Broader Data Source Integration: Expanding the solution to analyze data from additional sources, such as endpoint logs and cloud services, for a more comprehensive security posture.
• Collaboration with Threat Intelligence Platforms:** Integrating with external threat intelligence feeds to enrich detection capabilities and enhance response strategies.
• User Education and Training Modules: Developing modules to educate users on identified threats and best practices for cybersecurity.