Open Access

ARTICLE

Optimal Machine Learning Enabled Intrusion Detection in Cyber-Physical System Environment

by Bassam A. Y. Alqaralleh^1,*, Fahad Aldhaban¹, Esam A. AlQarallehs², Ahmad H. Al-Omari³

1 MIS Department, College of Business Administration, University of Business and Technology, Jeddah, 21448, Saudi Arabia
2 School of Engineering, Princess Sumaya University for Technology, Amman, 11941, Jordan
3 Faculty of Science, Computer Science Department, Northern Border University, Arar, 91431, Saudi Arabia

* Corresponding Author: Bassam A. Y. Alqaralleh. Email:

Computers, Materials & Continua 2022, 72(3), 4691-4707. https://doi.org/10.32604/cmc.2022.026556

Received 30 December 2021; Accepted 08 March 2022; Issue published 21 April 2022

Abstract

Cyber-attacks on cyber-physical systems (CPSs) resulted to sensing and actuation misbehavior, severe damage to physical object, and safety risk. Machine learning (ML) models have been presented to hinder cyberattacks on the CPS environment; however, the non-existence of labelled data from new attacks makes their detection quite interesting. Intrusion Detection System (IDS) is a commonly utilized to detect and classify the existence of intrusions in the CPS environment, which acts as an important part in secure CPS environment. Latest developments in deep learning (DL) and explainable artificial intelligence (XAI) stimulate new IDSs to manage cyberattacks with minimum complexity and high sophistication. In this aspect, this paper presents an XAI based IDS using feature selection with Dirichlet Variational Autoencoder (XAIIDS-FSDVAE) model for CPS. The proposed model encompasses the design of coyote optimization algorithm (COA) based feature selection (FS) model is derived to select an optimal subset of features. Next, an intelligent Dirichlet Variational Autoencoder (DVAE) technique is employed for the anomaly detection process in the CPS environment. Finally, the parameter optimization of the DVAE takes place using a manta ray foraging optimization (MRFO) model to tune the parameter of the DVAE. In order to determine the enhanced intrusion detection efficiency of the XAIIDS-FSDVAE technique, a wide range of simulations take place using the benchmark datasets. The experimental results reported the better performance of the XAIIDS-FSDVAE technique over the recent methods in terms of several evaluation parameters.

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Keywords

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