Overview

Modern enterprise networks face an escalating crisis in which the sophistication of cyber threats has outpaced the defensive capacity of traditional, centrally orchestrated intrusion detection systems (IDSs). Despite substantial advances in machine learning (ML) and deep learning (DL) approaches to anomaly detection, monolithic detectors routinely suffer from brittle generalization across novel attack families, unacceptably high false positive rates (FPRs) that exhaust analyst attention, single-point-of-failure vulnerabilities, and inadequate robustness against adversarial perturbation and poisoning. This dissertation investigates whether principles drawn from emergent swarm intelligence (SI)-specifically bio-inspired hierarchical decomposition, swarm-optimized adaptive learning, Byzantine-resilient consensus, and autonomous graduated response-can be integrated into a coherent defensive framework that delivers both measurable performance gains and verifiable robustness under adversarial conditions. The study introduces and empirically evaluates the Hierarchical Autonomous Cybersecurity Swarm (HACS), a three-tier architecture consisting of lightweight Sensor agents performing local feature extraction, Analyst agents executing heterogeneous ML-based detection, and Coordinator agents orchestrating Byzantine Fault Tolerant (BFT) consensus for final classification. Using a quantitative, multi-dataset experimental design, HACS was evaluated on four canonical intrusion-detection benchmarks (NSL-KDD, UNSW-NB15, CIC-IDS2017, CSE-CIC-IDS2018), compared against seven baseline classifiers, and stress-tested through adversarial scenarios including Byzantine agent corruption up to 30%, label-flipping poisoning up to 20%, and evasion attacks bounded by ε = 0.10. Results indicate that HACS attained accuracies of 77.36%, 76.23%, 93.43%, and 90.65% across the four datasets, with a 50.4% relative reduction in FPR on UNSW-NB15 and sustained accuracies of 82.52%, 74.48%, and 66.29% at 10%, 20%, and 30% Byzantine corruption, respectively. Throughput reached 10,000-16,000 samples per second with linear scalability to 18 agents. Friedman testing across benchmarks revealed marginal statistical superiority, suggesting that the framework's primary contribution lies less in raw accuracy than in combined robustness, operational resilience, and graduated automated response. Implications and boundary conditions of these findings for theory, practice, and future research are discussed.

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