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| 4.07 MB | Adobe PDF |
Advisor(s)
Abstract(s)
The next-generation of Industrial Automation and Control Systems (IACS) and Supervisory Control and Data Acquisition (SCADA) systems pose numerous challenges in terms of cybersecurity monitoring. We have been witnessing the convergence of OT/IT networks, combined with massively distributed metering and control scenarios such as smart grids. Larger and geographically widespread attack surfaces, and inherently more data to analyse, will become the norm. Despite several advances in recent years, domain-specific security tools have been facing the challenges of trying to catch up with all the existing security flaws from the past, while also accounting for the specific needs of the next-generation of IACS. Moreover, the aggregation of multiple techniques and sources of information into a comprehensive approach has not been explored in depth. Such a holistic perspective is paramount since it enables a global and enhanced analysis enabled by the usage, combination and aggregation of the outputs from multiple sources and techniques. This paper starts by providing a review of the more recent anomaly detection techniques for SCADA systems, focused on both theoretical machine learning approaches and complete frameworks. Afterwards, it proposes a complete framework for an Intrusion and Anomaly Detection System (IADS) composed of specific detection probes, an event processing layer and a core anomaly detection component, amongst others. Finally, the paper presents an evaluation of the framework within a large-scale hybrid testbed, and a comparison of different anomaly detection scenarios based on various machine learning techniques.
Description
Keywords
IACS Industrial control systems SCADA Cybersecurity Critical infrastructure protection Network anomaly detection Intrusion detection Event processing