SECURING CYBER-PHYSICAL SYSTEMS: FLAMINGO SEARCH ALGORITHM OPTIMIZED DEEP LEARNING FOR THREAT DETECTION

Threat detection in Cyber-Physical Systems (CPS) is essential to safeguarding the reliability and security of these integrated systems, which interface digital components with the physical world. CPS platforms, common in healthcare, industrial automation, smart cities, and transportation, face vulnerability to various cyber threats. Effective threat detection in CPS involves identifying and mitigating cybersecurity risks, which can otherwise disrupt physical operations, compromise data integrity, and jeopardize safety. Machine Learning (ML) and Deep Learning (DL) techniques are increasingly leveraged for detecting anomalies by modeling the CPS’s normal behaviour and recognizing deviations. This study presents an Automated Threat Detection using the Flamingo Search Algorithm with Optimal Deep Learning (ATD-FSAODL) in CPS environments. Initially, the ATD-FSAODL technique applies Flamingo Search Algorithm (FSA)-based feature subset selection to identify optimal feature sets. The ATD-FSAODL approach utilizes a modified Elman Spike Neural Network (MESNN) for threat recognition and classification, with the Slime Mold Algorithm (SMA) optimizing the MESNN parameters to enhance detection accuracy. Simulation experiments on benchmark databases demonstrate the effectiveness of the ATD-FSAODL technique, achieving a maximum accuracy of 99.58%, precision of 99.58%, recall of 99.58%, F-score of 99.58%, and MCC of 99.16%.