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

Abstract

Modern enterprise networks operate under persistent threats that exploit cloud-native misconfigurations, identity sprawl, and API vulnerabilities at machine speed. Existing security operations center (SOC) architectures remain largely reactive, signature-dependent, and incapable of predicting multi-stage lateral movement. This paper proposes the Cognitive Cyber Defense Digital Twin (CCDT), a unified architecture integrating federated learning (FL), graph neural network (GNN)-based attack-path forecasting, adversarially hardened detection models, and autonomous Security Orchestration, Automation, and Response (SOAR) with deception engineering. The CCDT constructs a continuously synchronized digital replica of organizational assets and employs reinforcement learning-based red agents to stress-test detection models. A federated intelligence mesh enables cross-organizational privacy-preserving gradient sharing. Experimental evaluations against CICIDS-2018 and LANL datasets demonstrate 52% faster attack-path detection, 41% reduction in false positive rate, and 60% reduction in mean-time-to-respond (MTTR) compared to traditional SOC baselines. Integrated Explainable AI (XAI) modules using SHAP values enable audit-ready compliance reporting. The CCDT represents a paradigm shift from reactive monitoring to predictive, autonomous, and privacy-preserving cyber defense for hybrid cloud environments.