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

Yifan Liu, Shancang Li*, Xinheng Wang, Li Xu

*Corresponding author for this work

Research output: Contribution to journalReview articlepeer-review

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 L1 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.

Original languageEnglish
Pages (from-to)1233-1261
Number of pages29
JournalCMES - Computer Modeling in Engineering and Sciences
Volume140
Issue number2
DOIs
Publication statusPublished - 2024

Keywords

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

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