Two new papers have been recently published from the BiSSL group first-authored and co-authored by its students resulting from a collaborative grant with Sandia National Lab and Dr. Kate Davis’ group in Electrical Engineering.

Highlights include:

1. A graph-embedding technique, Node2Vec, to capture neighborhood relationships with second-order biased random walks for risk assessment in cyber-physical power grids.
2. Highly realistic and synthetic cyber network topologies for power grids to perform our case studies.
3. Visualization of the risk assessment with various methods for a more comprehensive situational awareness for grid operators.

Modern power grids and other complex systems are a fusion of physical and cyber components, giving rise to intricate interdependencies. These interdependencies, however, also expose vulnerabilities that can be exploited by adversaries. This paper delves into the critical examination of these interconnections, inspired by Ecological Network Analysis (ENA) techniques. By drawing from ecological modeling, we aim to understand the role of cyber-physical interdependencies in the resilience of complex systems. We introduce various modeling methods, including bipartite and tripartite networks, to analyze and map these interdependencies in the context of the IEEE WSCC 9-bus and the ACTIV 200-bus case study. The paper explores how these models can identify key actors and assess network resilience. Through a detailed methodology, we apply ecological metrics and community identification techniques to comprehensively evaluate the system’s interactions. The findings offer insights into the interplay of cyber and physical elements in power grids and other complex systems. These analysis methods show that tripartite networks produce more information on indirect interactions within a complex network. Additionally, they provide detailed information on how disturbances could propagate in a cyber-physical power system. Denial of service scenarios for the WSCC 9-bus and the ACTIV 200-bus case studies are employed to support this conclusion.

Payne, E., S. Hossain-McKenzie, N. Jacobs, K. Davis, A. Layton. (2024) “Analyzing Cyber-Physical Modularity and Interdependence Using Bio-Inspired Graph Modeling.” IEEE Access. DOI: 10.1109/ACCESS.2024.3450368

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Abstract: Power systems are facing an increasing number of cyber incidents, potentially leading to damaging consequences to both physical and cyber aspects. However, the development of analytical methods for the study of large-scale power infrastructures as cyber-physical systems is still in its early stages. Drawing inspiration from machine-learning techniques, the authors introduce a method inspired by the principles of graph embedding that is tailored for quantitative risk assessment and the exploration of possible mitigation strategies of large-scale cyber-physical power systems. The primary advantage of the graph embedding approach lies in its ability to generate numerous random walks on a graph, simulating potential access paths. Meanwhile, it enables capturing high-dimensional structures in low-dimensional spaces, facilitating advanced machine-learning applications, and ensuring scalability and adaptability for comprehensive network analysis. By employing this graph embedding-based approach, the authors present a structured and methodical framework for risk assessment in cyber-physical systems. The proposed graph embedding-based risk analysis framework aims to provide a more insightful perspective on cyber-physical risk assessment and situation awareness for power systems. To validate and demonstrate its applicability, the method has been tested on two cyber-physical power system models: the Western System Coordinating Council (WSCC) 9-Bus System and the Illinois 200-Bus System, thereby showing its advantages in enhancing the accuracy of risk analysis and comprehensiveness of situational awareness.

Sun, S., H. Huang, E. Payne, S. Hossain-McKenzie, N. Jacobs, H. Vincent Poor, A. Layton, and K. Davis. (2024) “A Graph Embedding-Based Approach for Automatic Cyber-Physical Power System Risk Assessment to Prevent and Mitigate Threats at Scale.” The Institution of Engineering and Technology (IET) Cyber-Physical Systems: Theory & Applications. DOI: 10.1049/cps2.12097