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

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.

Explainable AI for Cyber-Physical Systems: Issues and Challenges

“…Fundamental cyber-physical system types in Industry 4.0 include smart factories, chatbots, human-computer interaction, smart healthcare, manufacturing, smart products, augmented reality, transportation industry, aviation, autonomous vehicles, smart consumer appliances, intelligent chemical industry, industrial robots, smart assistance, autonomous resource exploration, cybersecurity and privacy, predictive maintenance, and smart cities [2], [5], [13], [41]. Smart cities may employ diverse CPS such as smart transportation, smart airports, smart ports, smart hospitals, flood detection and mitigation, drainage monitoring systems, and smart power grids [34], [42].…”

“…Industrial CPS are particularly susceptible to cyberattacks with potentially dangerous consequences. According to Alqaralleh et al [41], "Cyber-attacks on cyber-physical systems (CPS) [result in] sensing and actuation misbehavior, severe damage to physical object, and safety risk." One type of commonly used attack against industrial manufacturing CPS is adaptive poisoning.…”

“…Studies have demonstrated the successful implementation of XAI for attack detection in cyber-physical systems [8], [9], [41], [50]. XAI also drives cybersecurity improvement in CPS by assuring accountability.…”

“…Cyberattacks on network-connected computing devices pose risks to information security, system performance, and privacy among many others [41]. However, the risks from intrusions in cyber-physical systems extend beyond the network and data into the physical realm.…”

Split-n-Swap: A New Modification of the Twofish Block Cipher Algorithm

“…A number of reviews has been introduced to compare between various symmetric and nonsymmetric cryptography algorithms [11][12][13][14][15][16][17][18][19].…”