Simplicial Graph-Based Detection and Localization of Cyber Attacks Against Large-Scale Smart Grids

Abstract

Ensuring the resilience of large-scale power systems against cyber attacks is critical to maintaining the stability of modern cyber-physical energy systems. Existing machine learning (ML)-based detection frameworks predominantly focus on pairwise node interactions (i.e., edges) and often overlook capturing higher-order topological structures (i.e., simplicial complexes) that emerge within power networks. This paper introduces a novel simplicial recurrent graph neural network (SRGNN) for attack detection and localization in large-scale smart grid infrastructure. Unlike conventional graph models, SRGNN incorporates higher-order simplicial interactions to capture group-wise dependencies among buses and transmission lines, allowing the model to better reflect the multi-scale dynamics of grid operation and control. We investigate the robustness of our model against benchmark and proposed complex simplicial-based attack node selection strategies. Our extensive experiments on large-scale transmission power networks (<inline-formula> <tex-math notation="LaTeX">$2{,}869$ </tex-math></inline-formula>-bus, <inline-formula> <tex-math notation="LaTeX">$9{,}241$ </tex-math></inline-formula>-bus, and <inline-formula> <tex-math notation="LaTeX">$70{,}000$ </tex-math></inline-formula>-bus systems) demonstrate that the proposed SRGNN model outperforms ML-based benchmark models, achieving superior detection and localization performance by 9–39% and 8–35%, respectively, in detection rate against complex attacks. These results underscore the importance of modeling higher-order topological structures for robust and scalable security in power systems.