aiqbench analysis
This paper bridges the gap between component-level machine learning robustness and system-level resilience certification for industrial control systems, formalizing how adversarial anomalies map to specific resilience constructs like absorption capacity and recovery trajectories. By deriving compositional bounds on heterogeneous detection networks, the authors demonstrate that hardening a single node does not guarantee overall security and reveal counterintuitive phenomena where isolated hardening can paradoxically reduce system-wide resilience. These findings provide critical theoretical foundations for designing certified, resilient AI-driven anomaly detection architectures in cyber-physical environments.
Towards a Resilience-Theoretic Foundation for Adversarial Robustness in Industrial Control System Anomaly Detection
arXiv:2609.07244v1 Announce Type: cross Abstract: Anomaly-based intrusion detection systems in industrial control systems (ICS) and operational technology (OT) environments are increasingly required to meet formal resilience criteria: absorbed adversarial disturbances, graceful degradation under sustained attack, and certified system-level guarantees. Existing resilience frameworks for cyber-physical systems define absorb-recover-adapt trajectories at the architectural level but do not treat machine learning anomaly detectors as first-class components, leaving a gap between component-level robustness evaluation and system-level resilience certification. In this paper, we establish that adversarial robustness in ICS anomaly detection is a specific instantiation of system resilience, and formalise this connection by mapping four resilience constructs, i.e. disturbance class, absorption capacity, recovery trajectory, and degradation function, onto the adversarial machine learning setting. We derive a compositional resilience bound for heterogeneous ICS detection networks, showing that the binding constraint on system-level resilience is the coupling-adjusted absorption capacity of each node along the attack path, not the per-node capacity -- so the binding node need not be the weakest one. Empirical validation on the BATADAL water distribution system benchmark demonstrates that the resulting metrics surface operationally significant phenomena invisible to standard benchmarks: the absorption-degradation divergence under adversarial training, and the paradox that hardening the binding node in isolation reduces system-level resilience. Implications for ICS architecture design and certification standards are discussed.
Abstract:Anomaly-based intrusion detection systems in industrial control systems (ICS) and operational technology (OT) environments are increasingly required to meet formal resilience criteria: absorbed adversarial disturbances, graceful degradation under sustained attack, and certified system-level guarantees. Existing resilience frameworks for cyber-physical systems define absorb-recover-adapt trajectories at the architectural level but do not treat machine learning anomaly detectors as first-class components, leaving a gap between component-level robustness evaluation and system-level resilience certification. In this paper, we establish that adversarial robustness in ICS anomaly detection is a specific instantiation of system resilience, and formalise this connection by mapping four resilience constructs, i.e. disturbance class, absorption capacity, recovery trajectory, and degradation function, onto the adversarial machine learning setting. We derive a compositional resilience bound for heterogeneous ICS detection networks, showing that the binding constraint on system-level resilience is the coupling-adjusted absorption capacity of each node along the attack path, not the per-node capacity -- so the binding node need not be the weakest one. Empirical validation on the BATADAL water distribution system benchmark demonstrates that the resulting metrics surface operationally significant phenomena invisible to standard benchmarks: the absorption-degradation divergence under adversarial training, and the paradox that hardening the binding node in isolation reduces system-level resilience. Implications for ICS architecture design and certification standards are discussed.
Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
arXiv:2609.07244 [cs.CR]
(or arXiv:2609.07244v1 [cs.CR] for this version)
https://doi.org/10.48550/arXiv.2609.07244 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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Full text under the publisher's open licence · 308 words
