arXiv:2605.27674v1 Announce Type: cross Abstract: Cyber-Physical Systems (CPS) integrate sensing, communication, computation, and control to support critical infrastructure, including smart grids, industrial automation, and control systems. In the electrical utility domain, various controllers are used in CPS to ensure the system detects and recovers from faults, such as voltage fluctuations, and to perform load balancing in distribution systems. Machine learning- and deep learning-based fault detection and localization frameworks have recently gained significant attention in CPS for their abi
The increasing reliance on AI/ML in critical infrastructure like Cyber-Physical Systems makes their security vulnerabilities, particularly backdoor attacks, a timely and urgent concern.
Sophisticated adversaries could exploit these vulnerabilities to compromise critical services, leading to widespread disruptions or even physical damage, affecting national security and economic stability.
The focus for securing cyber-physical systems expands beyond traditional cybersecurity measures to include the integrity and interpretability of the AI/ML models themselves, especially in fault detection.
- · AI security researchers
- · Cybersecurity firms specializing in CPS/OT
- · Developers of robust fault detection AI models
- · Operators of critical infrastructure with vulnerable AI systems
- · Nations with less sophisticated cybersecurity defenses
- · Vendors of insecure AI/ML solutions for CPS
Increased investment in AI security for critical infrastructure and more stringent regulatory oversight on deployed AI/ML systems in CPS.
Development of new AI-specific security standards and testing methodologies becomes essential before deploying ML-based fault detection in sensitive environments.
The potential for state-sponsored cyber warfare extends into manipulating critical infrastructure through embedded AI backdoors, posing a novel and complex threat to national security.
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