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

作     者：Bassam A.Y.Alqaralleh Fahad Aldhaban Esam A.AlQarallehs Ahmad H.Al-Omari 

作者机构：MIS DepartmentCollege of Business AdministrationUniversity of Business and TechnologyJeddah21448Saudi Arabia School of EngineeringPrincess Sumaya University for TechnologyAmman11941Jordan Faculty of ScienceComputer Science DepartmentNorthern Border UniversityArar91431Saudi Arabia 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2022年第72卷第9期

页      面：4691-4707页

核心收录：

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：Funding Statement: This Research was funded by the Deanship of Scientific Research at University of Business and Technology  Saudi Arabia 

主　　题：Cyber-physical systems explainable artificial intelligence deep learning security intrusion detection metaheuristics 

摘      要：Cyber-attacks on cyber-physical systems(CPSs)resulted to sensing and actuation misbehavior,severe damage to physical object,and safety *** 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 *** 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 *** developments in deep learning(DL)and explainable artificial intelligence(XAI)stimulate new IDSs to manage cyberattacks with minimum complexity and high *** this aspect,this paper presents an XAI based IDS using feature selection with Dirichlet Variational Autoencoder(XAIIDS-FSDVAE)model for *** proposed model encompasses the design of coyote optimization algorithm(COA)based feature selection(FS)model is derived to select an optimal subset of ***,an intelligent Dirichlet Variational Autoencoder(DVAE)technique is employed for the anomaly detection process in the CPS ***,the parameter optimization of the DVAE takes place using a manta ray foraging optimization(MRFO)model to tune the parameter of the *** order to determine the enhanced intrusion detection efficiency of the XAIIDS-FSDVAE technique,a wide range of simulations take place using the benchmark *** experimental results reported the better performance of the XAIIDSFSDVAE technique over the recent methods in terms of several evaluation parameters.