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Machine learning in industrial control system (ICS) security current landscape, opportunities and challenges1.79 MB
Published VersionCC BY V4.0, Open Access
Journal article
Machine learning in industrial control system (ICS) security: current landscape, opportunities and challenges
Journal of Intelligent Information Systems, Vol.60(3), pp.377-405
2023
Abstract
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.
Details
- Title
- Machine learning in industrial control system (ICS) security: current landscape, opportunities and challenges
- Authors
- Abigail Koay (Corresponding Author) - The University of QueenslandRyan K. L. Ko (Author) - The University of QueenslandHinne Hettema (Author) - The University of QueenslandKenneth Radke (Author) - The University of Queensland
- Publication details
- Journal of Intelligent Information Systems, Vol.60(3), pp.377-405
- Publisher
- Springer New York LLC
- Date published
- 2023
- DOI
- 10.1007/s10844-022-00753-1
- ISSN
- 1573-7675; 0925-9902
- Copyright note
- This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
- Grant note
- University of Queensland Cyber Security Initiative Grant
- Organisation Unit
- School of Science, Technology and Engineering
- Language
- English
- Record Identifier
- 99746198602621
- Output Type
- Journal article
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- Web Of Science research areas
- Computer Science, Artificial Intelligence
- Computer Science, Information Systems