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Detecting Anomalies in Programmable Logic Controllers Using Unsupervised Machine Learning

Abstract : Supervisory control and data acquisition systems have been employed for decades to communicate with and coordinate industrial processes. These systems incorporate numerous programmable logic controllers that manage the operations of industrial equipment based on sensor information. Due to the important roles that programmable logic controllers play in industrial facilities, these microprocessor-based systems are exposed to serious cyber threats.This chapter describes an innovative methodology that leverages unsupervised machine learning to monitor the states of programmable logic controllers to uncover latent defects and anomalies. The methodology, which employs a one-class support vector machine, is able to detect anomalies without being bound to specific scenarios or requiring detailed knowledge about the control logic. A case study involving a traffic light simulation demonstrates that anomalies are detected with high accuracy, enabling the prompt mitigation of the underlying problems.
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Submitted on : Tuesday, April 7, 2020 - 10:37:07 AM
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Chun-Fai Chan, Kam-Pui Chow, Cesar Mak, Raymond Chan. Detecting Anomalies in Programmable Logic Controllers Using Unsupervised Machine Learning. 15th IFIP International Conference on Digital Forensics (DigitalForensics), Jan 2019, Orlando, FL, United States. pp.119-130, ⟨10.1007/978-3-030-28752-8_7⟩. ⟨hal-02534603⟩

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