An equilibrium optimizer with deep recurrent neural networks enabled intrusion detection in secure cyber-physical systems

Cyber-physical systems (CPSs) are characterized by their integration of physical processes with computational and communication components. These systems are utilized in various critical infrastructure sectors, including energy, healthcare, transportation, and manufacturing, making them attractive targets for cyberattacks. Intrusion detection system (IDS) has played a pivotal role in identifying and mitigating cyber threats in CPS environments. Intrusion detection in secure CPSs is a critical component of ensuring the integrity, availability, and safety of these systems. The deep learning (DL) algorithm is extremely applicable for detecting cyberattacks on IDS in CPS systems. As a core element of network security defense, cyberattacks can change and breach the security of network systems, and then an objective of IDS is to identify anomalous behaviors and act properly to defend the network from outside attacks. Deep learning (DL) and Machine learning (ML) algorithms are crucial for the present IDS. We introduced an Equilibrium Optimizer with a Deep Recurrent Neural Networks Enabled Intrusion Detection (EODRNN-ID) technique in the Secure CPS platform. The main objective of the EODRNN-ID method concentrates mostly on the detection and classification of intrusive actions from the platform of CPS. During the proposed EODRNN-ID method, a min-max normalization algorithm takes place to scale the input dataset. Besides, the EODRNN-ID method involves EO-based feature selection approach to choose the feature and lessen high dimensionality problem. For intrusion detection, the EODRNN-ID technique exploits the DRNN model. Finally, the hyperparameter related to the DRNN model can be tuned by the chimp optimization algorithm (COA). The simulation study of the EODRNN-ID methodology is verified on a benchmark data. Extensive results display the significant performance of the EODRNN-ID algorithm when compared to existing techniques.