Enhancing Cyber-Physical System Security through AI-Driven Intrusion Detection and Blockchain Integration - N. Purandhar; M. Rajendrian; Ahmed Mudassar Ali; M. Sangeetha; Mukesh Soni; D. Arul Kumar (International Journal of Computational and Experimental Science and Engineering, 2025)
Cyber-Physical Systems (CPS) play a critical role in modern industries, smart grids, healthcare, and autonomous transportation. However, their increasing connectivity makes them vulnerable to cyber threats. This research proposes an AI-driven Intrusion Detection System (AI-IDS) integrated with Blockchain Technology to enhance CPS security. The AI-IDS employs deep learning models for anomaly detection, leveraging graph-based machine learning and federated learning to improve real-time threat mitigation. Additionally, blockchain ensures data integrity, access control, and decentralized security through smart contracts and consensus mechanisms. The framework is validated using real-world CPS datasets, demonstrating improved detection accuracy, reduced false alarms, and resilience against adversarial attacks. This hybrid approach enhances scalability, trustworthiness, and real-time defense in cyber-physical environments