Two journal papers related to the use of bio-inspired system design approaches for cyber-physical systems from the BiSSL group have recently been accepted for publication! The 1st stems directly from a current ongoing grant with Sandia National Labs with BiSSL Ph.D. student Emily Payne as co-author and the 2nd is a culmination of multiple collaborations across mechanical and electrical engineering at Texas A&M and is led by former BiSSL Ph.D. student Abheek Chatterjee, now a post-doc at NIST.

Abstract: Cyber-physical systems have behavior 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 cyber-physical systems requires improved understanding of the combined cyber-physical system behavior and methods for holistic analysis. Therefore, we propose leveraging clustering techniques on cyber-physical data from smart grid systems to analyze differences and similarities in behavior 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 cyber-physical systems. Through this analysis, deeper insights can be shared with decision-makers on what cyber and physical components are strongly or weakly linked, what cyber-physical pathways are most traversed, and the criticality of certain cyber-physical nodes or edges. This paper presents several types of clustering methods for cyber-physical graphs of smart grid systems and their application in assessing different types of disturbances for informing cyber-physical situational awareness. The collection of these clustering techniques provide a foundational basis for cyber-physical graph interdependency analysis.

Jacobs, N., S. Hossain-McKenzie, S. Sun, E. Payne, A. Summers, L. Al Homoud, A. Layton, K. Davis, and C. Goes. (2024) “Leveraging Clustering Techniques for Cyber-Physical System Analysis to Enhance Disturbance Characterization.” The Institution of Engineering and Technology (IET) Cyber-Physical Systems: Theory & Applications.

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Abstract: The design of resilient infrastructure is a critical engineering challenge for the smooth functioning of society. These networks are best described as Cyber-Physical Systems of Systems (CPSoS): integration of independent constituent systems, connected by physical and cyber interactions, to achieve novel capabilities. Bio-inspired design, using a framework called the Ecological Network Analysis (ENA), has been shown to be a promising solution for improving the resilience of engineering networks. However, the existing ENA framework can only account for one type of flow in a network. Thus, it is not yet applicable for the evaluation of CPSoS. The present work addresses this limitation by proposing a novel multigraph model of CPSoS, along with guidelines and modified metrics that enable ENA evaluation of the overall (cyber and physical) network organization of the CPSoS. The application of the extended framework is demonstrated using an energy infrastructure case study. This research lays the critical groundwork for investigating the design of resilient CPSoS using biological ecosystems inspiration.

Chatterjee, A., H. Huang, R. Malak, K. Davis, and A. Layton. (2024) “Extending Ecological Network Analysis to Design Resilient Cyber-Physical System of Systems.” IEEE Open Journal of Systems Engineering.