Abstract
Machine learning has been widely studied in the security analysis of Industrial Control Systems (ICSs). However, in industrial scenarios, the amount of data as well as the speed of data generation are very different from standard machine learning data sets. Using these heterogeneous data and finding meaningful insights for practical security applications in ICSs is a big challenge. In addition, ICSs have been built for quite a long time. Security has not been seriously taken into account when ICSs were built. Security assessment or attack prevention cannot always be done in real time, as an ICS requires to be online all the time, especially when it comes to systems that affect critical infrastructure. In this work, we are motivated to a provide a clear and comprehensive survey of the state-of-the-art work that employs machine learning in security applications in ICSs, including vulnerability analysis, vulnerability detection and exploitation, anomaly detection and security assessment. Based on our in-depth survey, we highlight the issues of industrial protocol analysis with machine learning methods, provide the security applications with machine learning in ICSs and indicate the future directions.
Highlights
Industrial control systems have been widely used in various control domains, such as energy, municipal, water conservancy, railway, petroleum and petrochemical, etc., which are indispensable for the stable operation of the current industry
We provide the applications of machine learning algorithms on industrial protocols for security analysis, vulnerability exploitation detection, and security assessment
We provide four methods to perform the assessment of the Industrial Control Systems (ICSs) protocols and make a comparative analysis of these methods
Summary
Industrial control systems have been widely used in various control domains, such as energy, municipal, water conservancy, railway, petroleum and petrochemical, etc., which are indispensable for the stable operation of the current industry. J. Men et al.: Machine Learning Methods for Industrial Protocol Security Analysis: Issues, Taxonomy, and Directions and computer manufacturers, which lead to a prosperous development era. There is a large amount of research work using machine learning algorithms for security analysis, exploitation detection and security assessment of industrial protocols. We provide the applications of machine learning algorithms on industrial protocols for security analysis, vulnerability exploitation detection, and security assessment. Based on our in-depth analysis of machine learning methods for industrial protocol security analysis, we summarize their issues and taxonomy, and provide a comprehensive perspective for future research work.
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