Machine learning for intrusion detection in industrial control systems: Applications, challenges, and recommendations

Muhammad Azmi Umer, Khurum Nazir Junejo, Muhammad Taha Jilani and Aditya P. Mathur

International Journal of Critical Infrastructure Protection, 2022, vol. 38, issue C

Abstract: Methods from machine learning are used in the design of secure Industrial Control Systems. Such methods focus on two major areas: detection of intrusions at the network level using the information acquired through network packets, and detection of anomalies at the physical process level using data that represents the physical behavior of the system. This survey focuses on four types of methods from machine learning for intrusion and anomaly detection, namely, supervised, semi-supervised, unsupervised, and reinforcement learning. The literature available in the public domain was carefully selected, analyzed, and placed along a 10-dimensional space for ease of comparison. This multi-dimensional approach is found valuable in the comparison of the methods considered and enables a scientific discussion on their utility in specific environments. The challenges associated in using machine learning, and gaps in research, are identified and recommendations made.

Keywords: Machine learning; Deep learning; Intrusion detection; Anomaly detection; Cyber-attacks; Cyber physical systems; Critical infrastructures; IoT; Industrial Control Systems (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ijocip:v:38:y:2022:i:c:s1874548222000087

DOI: 10.1016/j.ijcip.2022.100516

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