AI-Powered Cyber Warfare and the Evolution of Zero Trust Security Architectures in Autonomous Networks

Abstract

The increasing sophistication of intelligent cyber warfare, in which adversaries exploit artificial intelligence to automate reconnaissance, generate polymorphic malware, and conduct machine-speed attacks, has rendered conventional perimeter security and static Zero Trust implementations inadequate for autonomous and self-managing networks. This study addresses the absence of an integrated, adaptive architecture by designing and analytically evaluating an AI-driven adaptive Zero Trust framework that unifies behavioural analytics, federated intrusion detection, explainable trust scoring, and autonomous policy enforcement within a NIST-aligned model. Adopting a quantitative, experimental, and simulation-based design, the framework was evaluated using public benchmark datasets including CICIDS2017, UNSW NB15, and BoT IoT, with standardised preprocessing, balanced resampling, and stratified cross-validation. Eight classifiers were trained, among which gradient boosting achieved an accuracy and F1 score of approximately 0.9999 on the CICIDS2017 benchmark after leakage-prone identifier features were removed, while ensemble and convolutional models performed strongly. The dynamic trust engine, exercised on an illustrative cohort of ten simulated agents, enforced conservative session-level access decisions, and a simulated three-node federated learning configuration produced an aggregated F1 score of 0.9185, quantifying the privacy-performance trade-off under heterogeneous partitions. These findings support a coherent, identity-centric defence delivering continuous, explainable verification. The study contributes a conceptual architectural blueprint validated through simulation rather than an operationally deployed system, and sampled datasets and simulated agents constrain operational generalization, motivating future validation on live autonomous network testbeds.