Cyber Defense Digital Twins: A Federated Learning and Zero-Trust AI Architecture for Autonomous Threat Prediction and Response

This research project explores the development of a Cognitive Cyber Defense Digital Twin (CCDT) framework designed to address the vulnerabilities of modern enterprise networks against multi-stage cyber threats. By constructing a continuously synchronized digital replica of organizational assets, the proposed architecture integrates federated learning (FL) and graph neural network (GNN) methodologies to forecast complex attack paths in real time. To enhance resilience, the system incorporates reinforcement learning-based red agents that continuously stress-test detection models, alongside an autonomous Security Orchestration, Automation, and Response (SOAR) engine coupled with deception engineering. A federated intelligence mesh is utilized to facilitate privacy-preserving gradient sharing across multiple organizations. The framework's performance is evaluated using benchmark datasets,

specifically CICIDS-2018 and LANL, focusing on metrics such as attack-path detection speed, false positive rate reduction, and mean-time-to-respond (MTTR). Furthermore, explainable artificial intelligence (XAI) modules leveraging SHAP values are integrated to provide interpretable decision-making pathways for audit-ready compliance. This research offers comprehensive implementation support and methodology guidance for developing predictive, autonomous, and zero-trust cyber defense systems in hybrid cloud environments.

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