AI-Enhanced Intrusion Detection with Blockchain for Securing Cyber-Physical Systems

Cyber-Physical Systems (CPS) are essential to critical infrastructures such as smart grids, h ealth care, autonomous vehicles, and industrial automation. Their integration of computational and physical processes delivers efficiency but also introduces significant cybersecurity risks. Traditional intrusion detection systems (IDS) often lack scalability, adaptability, and robustness against advanced cyberattacks. This work proposes a hybrid framework combining an AI-driven Intrusion Detection System (AI-IDS) with blockchain technology. The AI-IDS uses deep learning for anomaly detection, enhanced with graph-based learning to capture complex CPS interactions and federated learning for distributed, privacy-preserving defense. Blockchain ensures data integrity, access control, and decentralized trust through smart contracts and consensus mechanisms.Evaluation on CPS datasets shows improved detection accuracy, reduced false alarms, and resilience against adversarial threats. By merging AI adaptability with blockchain trust guarantees, the approach provides a scalable, transparent, and real-time defense mechanism, strengthening CPS security in safety-critical applications.