A Systematic Review of AI-Driven and Quantum-Resistant Security Solutions for Cyber-Physical Systems: Blockchain, Federated Learning, and Emerging Technologies
Abstract
Cyber-Physical Systems (CPS) form the foundation of critical infrastructures, integrating computing, networking, and physical processes across sectors such as energy, healthcare, and manufacturing. As CPS become increasingly interconnected, they face sophisticated cyber threats that challenge traditional security measures. This systematic review explores next-generation cybersecurity solutions designed to protect CPS, focusing on emerging technologies like Artificial Intelligence (AI), blockchain, quantum-resistant cryptography, and federated learning (FL). We conducted a comprehensive literature search across IEEE Xplore, Scopus, and Web of Science, to identify studies published between 2010 and 2024. The review covers studies that examine the role of AI in anomaly detection and automated threat responses, blockchain for decentralized communication and identity management, and quantum-resistant cryptographic techniques as a safeguard against future quantum-enabled attacks. Federated learning, which enables collaborative learning across CPS devices while preserving data privacy, is also evaluated for its distributed security applications. The findings indicate that while AI enhances real-time security, it remains vulnerable to adversarial attacks. Blockchain offers robust security but faces scalability challenges, and federated learning requires stronger safeguards against model poisoning.
How strong is the evidence?
Evidence Hub reads this as a systematic review. Its evidence level is 1 of 5 (Highest · pooled evidence). Pooled evidence from several studies sits at the top of the evidence pyramid.
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