Machine learning in industrial control system (ICS) security: current landscape, opportunities and challenges

The advent of Industry 4.0 has led to a rapid increase in cyber attacks on industrial systems and processes, particularly on Industrial Control Systems (ICS). These systems are increasingly becoming prime targets for cyber criminals and nation-states looking to extort large ransoms or cause disruptions due to their ability to cause devastating impact whenever they cease working or malfunction. Although myriads of cyber attack detection systems have been proposed and developed, these detection systems still face many challenges that are typically not found in traditional detection systems. Motivated by the need to better understand these challenges to improve current approaches, this paper aims to (1) understand the current vulnerability landscape in ICS, (2) survey current advancements of Machine Learning (ML) based methods with respect to the usage of ML base classifiers (3) provide insights to benefits and limitations of recent advancement with respect to two performance vectors; detection accuracy and attack variety. Based on our findings, we present key open challenges which will represent exciting research opportunities for the research community.

Automated summary

The advent of Industry 4.0 has led to an increase in cyber attacks on industrial systems, especially Industrial Control Systems (ICS). Existing cyber attack detection systems face unique challenges in ICS. This paper aims to understand the vulnerability landscape in ICS, survey advancements in Machine Learning-based methods, and provide insights on detection accuracy and attack variety, presenting open challenges for future research.