MVRO-based DRNN: multi-verse rider optimisation-based deep recurrent neural network for intrusion detection in latency constrained cyber physical systems

Arvind Kamble and Virendra S. Malemath

International Journal of Information and Decision Sciences, 2025, vol. 17, issue 1, 110-131

Abstract: The cyber attacks on cyber physical system leads to actuation and sensing behaviour, safety risks, and rigorous damages to the physical object. Therefore, in this paper, multi-verse rider optimisation (MVRO)-based deep recurrent neural network (DRNN) is devised for identifying intrusions in latency-constrained cyber physical systems. In the latency-constrained cyber physical system, the process is carried out using three layers, end point layer, cloud layer, and fog layer. Here, the feature extraction process is performed using the water wave-based improved rider optimisation algorithm (WWIROA) for the classification process. The MVRO approach is the combination of the rider optimisation algorithm (ROA), and multi-verse optimiser (MVO). The DRNN classifier is utilised for the intrusion detection process. In addition, the DRNN classifier is trained using the introduced MVRO technique for better performance. Furthermore, the MVRO-based DRNN technique achieves low latency of 19.23 s, high specificity, sensitivity, and accuracy of 0.929, 0.974, and 0.956, respectively.

Keywords: intrusion detection; cyber physical system; cloud layer; deep recurrent neural network; DRNN; multi-verse optimiser; rider optimisation algorithm; ROA. (search for similar items in EconPapers)
Date: 2025
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