A Robust Explainable Intrusion Detection Framework Using Behavioural Manifold Analysis, Spatiotemporal Graph Learning, Adversarial Calibration, & Topological Stability Operations

Abstract

Modern network infrastructures need to have intrusion detection systems that continue to operate accurately when there are topology changes, as well as adversarial manipulations of the system; when different layers of the communication protocols interact with each other; and when new types of behaviors occur in the system. Machine Learning techniques typically analyze individual flows, use a static threshold, do not give much structure, and also cannot explain how cross-layer attacks propagate through the system. In order to improve upon current limitations, this research proposes a predictive model integration technique utilizing the combination of Spatiotemporal Graph Convolutional Ensemble Validation for topology aware risk estimation; Recursive Multi Resolution Adversarial Calibration for confidence correction against evasive attacks; Topological Perturbation Stability Analysis for evaluating robustness; Hierarchical Attention Diffusion Validation for identifying anomalies at the protocol layer; and Behavioral Manifold Conformity Indexing for detecting outliers (zero-days) in the system. The sequence nature of this proposed architecture allows for the preservation of information flow from one level of abstraction (structural), to another (statistical), then to others (adversarial, hierarchical, behavioral). This allows for improvement in reliability, interpretation, and deployability. Preliminary results indicate an accuracy of 98.1%, an F1 score of 97.4%, an AUROC value of 98.8%, an average false positive rate of 2.9%, an adversary detection accuracy of 95. 2%, and a zero day detection accuracy of 93.1% in real time scenarios.