TY  - EJOU
AU  - Liu, Yifan 
AU  - Li, Shancang 
AU  - Wang, Xinheng 
AU  - Xu, Li 

TI  - A Review of Hybrid Cyber Threats Modelling and Detection Using Artificial Intelligence in IIoT
T2  - Computer Modeling in Engineering \& Sciences

PY  - 2024
VL  - 140
IS  - 2
SN  - 1526-1506

AB  - The Industrial Internet of Things (IIoT) has brought numerous benefits, such as <i>improved efficiency</i>, <i>smart analytics</i>, and <i>increased automation</i>. 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.
KW  - Cyber security; Industrial Internet of Things; artificial intelligence; machine learning algorithms; hybrid cyber threats

DO  - 10.32604/cmes.2024.046473