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Open Access

REVIEW

A Review of Hybrid Cyber Threats Modelling and Detection Using Artificial Intelligence in IIoT

Yifan Liu1, Shancang Li1,*, Xinheng Wang2, Li Xu3
1 School of Computer Science and Informatics, Cardiff University, Cardiff, CF24 4AG, UK
2 Department of Mechatronics and Robotics, Xi’an Jiaotong-Liverpool University, Suzhou, 215123, China
3 Department of Information Technology & Decision Sciences, Old Dominion University, Norfolk, 23529, USA
* Corresponding Author: Shancang Li. Email: email
(This article belongs to the Special Issue: Machine Learning Empowered Distributed Computing: Advance in Architecture, Theory and Practice)

Computer Modeling in Engineering & Sciences https://doi.org/10.32604/cmes.2024.046473

Received 02 October 2023; Accepted 21 December 2023; Published online 26 April 2024

Abstract

The Industrial Internet of Things (IIoT) has brought numerous benefits, such as 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 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.

Keywords

Cyber security; Industrial Internet of Things; artificial intelligence; machine learning algorithms; hybrid cyber threats

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