Leveraging graph clustering techniques for cyberphysical system analysis to enhance disturbance characterisation
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AbstractCyberphysical systems have behaviour that crosses domain boundaries during events such as planned operational changes and malicious disturbances. Traditionally, the cyber and physical systems are monitored separately and use very different toolsets and analysis paradigms. The security and privacy of these cyberphysical systems requires improved understanding of the combined cyberphysical system behaviour and methods for holistic analysis. Therefore, the authors propose leveraging clustering techniques on cyberphysical data from smart grid systems to analyse differences and similarities in behaviour during cyber, physical, and cyberphysical disturbances. Since clustering methods are commonly used in data science to examine statistical similarities in order to sort large datasets, these algorithms can assist in identifying useful relationships in cyberphysical systems. Through this analysis, deeper insights can be shared with decisionmakers on what cyber and physical components are strongly or weakly linked, what cyberphysical pathways are most traversed, and the criticality of certain cyberphysical nodes or edges. This paper presents several types of clustering methods for cyberphysical graphs of smart grid systems and their application in assessing different types of disturbances for informing cyberphysical situational awareness. The collection of these clustering techniques provide a foundational basis for cyberphysical graph interdependency analysis.
