Improving SIEM for critical SCADA water infrastructures using machine learning

Hindy, Hanan, Brosset, David, Bayne, Ethan, Seeam, Amar ORCID: https://orcid.org/0000-0001-8203-1545 and Bellekens, Xavier (2019) Improving SIEM for critical SCADA water infrastructures using machine learning. Katsikas, Sokratis K., Cuppens, Frédéric, Cuppens, Nora, Lambrinoudakis, Costas, Antón, Annie, Gritzalis, Stefanos, Mylopoulos, John and Kalloniatis, Christos, eds. Computer Security: ESORICS 2018 International Workshops, CyberICPS 2018 and SECPRE 2018, Barcelona, Spain, September 6–7, 2018, Revised Selected Papers. In: Fourth Workshop on Security of Industrial Control Systems and Cyber-Physical Systems (CyberICPS 2018), 06-07 Sept 2018, Barcelona, Spain. ISBN 9783030127855, e-ISBN 9783030127862. ISSN 0302-9743 [Conference or Workshop Item] (doi:10.1007/978-3-030-12786-2_1)

Abstract

Network Control Systems (NAC) have been used in many industrial processes. They aim to reduce the human factor burden and efficiently handle the complex process and communication of those systems. Supervisory control and data acquisition (SCADA) systems are used in industrial, infrastructure and facility processes (e.g. manufacturing, fabrication, oil and water pipelines, building ventilation, etc.) Like other Internet of Things (IoT) implementations, SCADA systems are vulnerable to cyber-attacks, therefore, a robust anomaly detection is a major requirement. However, having an accurate anomaly detection system is not an easy task, due to the difficulty to differentiate between cyber-attacks and system internal failures (e.g. hardware failures). In this paper, we present a model that detects anomaly events in a water system controlled by SCADA. Six Machine Learning techniques have been used in building and evaluating the model. The model classifies different anomaly events including hardware failures (e.g. sensor failures), sabotage and cyber-attacks (e.g. DoS and Spoofing). Unlike other detection systems, our proposed work helps in accelerating the mitigation process by notifying the operator with additional information when an anomaly occurs. This additional information includes the probability and confidence level of event(s) occurring. The model is trained and tested using a real-world dataset.

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