Explainable Artificial Intelligence Enabled Intrusion Detection Technique for Secure Cyber-Physical Systems

A cyber-physical system (CPS) can be referred to as a network of cyber and physical components that communicate with each other in feedback manner. CPS is essential for daily activities approves critical infrastructure it provides the base innovative smart devices. The recent advances field explainable artificial intelligence have contributed development robust intrusion detection modes environments. This study develops an Explainable Artificial Intelligence Enabled Intrusion Detection Technique Secure Cyber-Physical Systems (XAIID-SCPS). proposed XAIID-SCPS technique mainly concentrates on classification intrusions platform. In technique, Hybrid Enhanced Glowworm Swarm Optimization (HEGSO) algorithm applied feature selection purposes. For detection, Improved Elman Neural Network (IENN) model was utilized Fruitfly (EFFO) parameter optimization. Moreover, integrates XAI approach LIME better understanding explainability black-box method accurate intrusions. simulation values demonstrate promising performance over approaches maximum accuracy 98.87%.