Machine learning approaches to cybersecurity in the industrial internet of things: a review - Heier, Melanie; Prasad, Penatiyana W. Chandana; Sayeed, Md Shohel (International Journal of Electrical and Computer Engineering (IJECE), 2025)
The industrial internet of things (IIoT) is increasingly used within various sectors to provide innovative business solutions. These technological innovations come with additional cybersecurity risks, and machine learning (ML) is an emerging technology that has been studied as a solution to these complex security challenges. At time of writing, to the author’s knowledge, a review of recent studies on this topic had not been undertaken. This review therefore aims to provide a comprehensive picture of the current state of ML solutions for IIoT cybersecurity with insights into what works to inform future research or real-world solutions. A literary search found twelve papers to review published in 2021 or later that proposed ML solutions to IIoT cybersecurity concerns. This review found that federated learning and semi-supervised learning in particular are promising ML techniques being proposed to combat the concerns around IIoT cybersecurity. Artificial neural network approaches are also commonly proposed in various combinations with other techniques to ensure fast and accurate cybersecurity solutions. While there is not currently a consensus on the best ML techniques to apply to IIoT cybersecurity, these findings offer insight into those approaches currently being utilized along with gaps where further examination is required.