AI-Powered Security-Aware Reconfiguration in Cyber-Physical Systems for Smart Healthcare and Energy Domains

Abstract

Cyber-physical systems (CPS) in smart energy and smart healthcare must continue to be safe, fast, and reliable, even in the face of evolving cyber threats and fluctuating network/edge conditions. An AI-based, security-aware framework of CPS reconfiguration is presented in this paper. It is comprised of (i) privacy-preserving federated anomaly detection at distributed edge nodes, (ii) a reinforcement-learning decision module that selects risk-aware reconfiguration actions under the objectives of latency, energy, and safety, and (iii) auditability, backed by blockchain, to provide trustworthy governance and accountability in the aftermath of the event. The detection module learns behavioral baselines at the local level and shares only protected updates to support data minimization and privacy compliance while still achieving high accuracy in heterogeneous deployments. The RL controller dynamically modifies actions to reduce service disruption, quicken recovery, and avert unsafe control transitions. A lightweight operational ledger captures reconfiguration activities and trust updates to provide auditable governance in multi-stakeholder CPS settings. In energy and healthcare CPS scenarios, there is strong operational performance. The system achieves 97.8% detection accuracy and an F1-score of 97.4% with an end-to-end latency of 41ms, coupled with a reconfiguration time of 65ms and a mean time to recovery of 5.6 seconds. The framework provides 99.4% uptime, consumes approximately 10 W at the edge, and with low error rates (2.1% false positive and 1.0% false negative), achieves 19.5 Mbps secure throughput. These results demonstrate that self-reconfigurable CPS can maintain mission-critical operational continuity while enhancing privacy, scalability, and governance in large-scale deployments.