Document Type

Article

Publication Date

2024

DOI

10.32604/cmes.2024.046473

Publication Title

Computer Modeling in Engineering & Sciences

Volume

140

Issue

2

Pages

1233-1261

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.

Rights

© 2024 The Authors.

This work is licensed under a Commons Attribution 4.0 International (CC BY 4.0) License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Original Publication Citation

Liu, Y. F., Li, S. C., Wang, X. H., & Xu, L. (2024). A review of hybrid cyber threats modelling and detection using artificial intelligence in IIoT. Computer Modeling in Engineering & Sciences, 140(2), 1233-1261. https://doi.org/10.32604/cmes.2024.046473

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