A review of hybrid cyber threats modelling and detection using artificial intelligence in IIoT

Liu, Yifan, Li, Shancang, Wang, Xinheng and Xu, Li 2024. A review of hybrid cyber threats modelling and detection using artificial intelligence in IIoT. Computer Modeling in Engineering & Sciences 140 (2) , pp. 1233-1261. 10.32604/cmes.2024.046473

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Abstract

The Industrial Internet of Things (IIoT) has brought numerous benefits, such as \textit{improved efficiency, smart analytics, and increased automation.} However, it also exposes connected devices, users, applications, and data generated to cyber security threats that need to be addressed. This work investigates hybrid cyber threats (HCTs), which are now working on an entirely new level with the increasingly adopted IIoT. {This work focuses} on emerging methods to model, detect, and defend against hybrid cyber attacks using machine learning (ML) techniques. Specifically, a novel ML-based HCT modelling and analysis framework was proposed, in which $L_1$ regularisation and Random Forest were used to cluster features and analyse the importance and impact of each feature in both individual threats and HCTs. {A grey relation analysis-based model was} employed to construct the correlation between IIoT components and different threats.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Computer Science & Informatics
Publisher:	Tech Science Press
ISSN:	1526-1492
Date of First Compliant Deposit:	22 January 2024
Date of Acceptance:	21 December 2023
Last Modified:	24 Jun 2024 13:09
URI:	https://orca.cardiff.ac.uk/id/eprint/165725

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