Asian Journal of Research in Computer Science

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.