Locational Detection of False Data Injection Attacks in the Edge Space via Hodge Graph Neural Network for Smart Grids

Recently, the emerging False Data Injection Attacks (FDIAs), one of the major cybersecurity threats, have been severely threatening smart grids, since FDIAs could bypass conventional bad data detectors to disrupt power system operations. To maintain the security of power systems, it is critical to develop efficient locational detectors for FDIAs. However, designing FDIA detectors that could model the inherent underlying graph structures of smart grids and spatially correlated measurement data residing on both branches and buses such that FDIAs in the edge space could be detected and located, remains an open problem. In this work, we propose an alternative graph representation for smart grids, regarding both the power flows on branches and power injections on buses, such that we could simultaneously process these data in the edge space. We propose a Hodge Aggregation Graph Neural Network (AGNN)-based FDIA detector, leveraging the Hodge theory and exploiting the Hodge Laplacian into the AGNN. We further develop a Hodge Aggregation Graph Attention Network (AGAT)-based FDIA detector to enhance the locational detection performance of the Hodge AGNN-based detector, by utilizing the graph attention mechanism. Illustrative simulation results demonstrate the superior locational detection performance of the proposed detectors, compared to the other state-of-the-art FDIA detectors.