African buffalo optimization with deep learning-based intrusion detection in cyber-physical systems

Cyber-physical system (CPS) incorporates several computing resources, networking units, interconnected physical processes, and monitoring the development and application of the computing system. Interconnection between the cyber and physical worlds initiates attacks on security problems, particularly with the enhancing complications of transmission networks. Despite the efforts to combat these problems, analyzing and detecting cyber-physical attacks from the complex CPS is challenging. Machine learning (ML)-researcher workers implemented based techniques to examine cyber-physical security systems. A competent network intrusion detection system (IDS) is essential to avoid these attacks. Generally, IDS uses ML techniques to classify attacks. However, the features used for classification are not frequently appropriate or adequate. Moreover, the number of intrusions is much lower than that of non-intrusions. This research presents an African Buffalo Optimizer Algorithm with a Deep Learning Intrusion Detection (ABOADL-IDS) model in a CPS environment. The main intention of the ABOADL-IDS model is to utilize the FS with an optimal DL approach for the intrusion recognition and identification procedure. Initially, the ABOADL-IDS model performs the data normalization process. Furthermore, the ABOADL-IDS model utilizes the ABO technique for feature selection. Moreover, the stacked deep belief network (SDBN) technique is employed for intrusion detection and identification. To improve the SDBN technique solution, the seagull optimization (SGO) technique is implemented for the hyperparameter selection. The assessment of the ABOADL-IDS technique is accomplished under NSLKDD2015 and CICIDS2015 datasets. The performance validation of the ABOADL-IDS technique illustrated a superior accuracy value of 99.28% over existing models concerning various measures.