Robustness and Certified Safety in Adversarial Machine Learning for Critical Infrastructure
Keywords:
adversarial machine learning, critical infrastructure, certified safety, robustness, formal verification, socio-technical systems, governance, resilience engineeringAbstract
The integration of machine learning into critical infrastructure systems, including power grids, water distribution networks, transportation control, and healthcare delivery, has introduced unprecedented operational efficiencies and predictive capabilities. However, this integration has simultaneously exposed these systems to adversarial threats that exploit vulnerabilities in learned models. This paper presents a comprehensive examination of adversarial machine learning in the context of critical infrastructure, with a focus on robustness and certified safety as dual imperatives. We argue that traditional approaches to adversarial defense, which often prioritize empirical robustness through heuristic augmentation, are insufficient for infrastructure contexts where failure carries catastrophic consequences. Instead, we advocate for a systems-level framework that incorporates certified defenses, formal verification, and structural redundancy as foundational components. The paper explores the architectural trade-offs between model complexity and certifiability, the governance challenges of deploying certified models in legacy infrastructure, and the policy implications of adversarial risk in public goods systems. Through cross-domain case analysis and forward-looking synthesis, we demonstrate that certified safety must be understood not merely as a technical property but as a socio-technical contract between system operators, regulators, and the public. We further examine how federated learning paradigms, such as those explored in recent privacy-preserving enterprise systems, inform the distributed governance of adversarial robustness. The paper concludes with a set of design principles and research priorities for building critical infrastructure machine learning systems that are both robust to adversarial manipulation and certifiably safe under formal guarantees.
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