Spatio-temporal Graph-Based Generation and Detection of Adversarial False Data Injection Evasion Attacks in Smart Grids

Smart power grids are vulnerable to security threats due to their cyber-physical nature. Existing data-driven detectors aim to address simple traditional false data injection attacks (FDIAs). However, adversarial false data injection evasion attacks (FDIEAs) present a more serious threat as adversaries, with different levels of knowledge about the system, inject adversarial samples to circumvent the grid’s attack detection system. The robustness of state-of-the-art graph-based detectors has not been investigated against sophisticated FDIEAs. Hence, this paper answers three research questions: (a) What is the impact of utilizing spatio-temporal features to craft adversarial samples and how to select attack nodes? (b) How can adversaries generate surrogate spatio-temporal data when they lack knowledge about the system topology? (c) What are the required model characteristics for a robust detection against adversarial FDIEAs? To answer the questions, we examine the robustness of several detectors against five attack cases and conclude the following: (a) Attack generation with full knowledge using spatio-temporal features leads to 5 − 26% and 2 − 5% higher degradation in detection rate (DR) compared to traditional FDIAs and using temporal features, respectively, whereas centrality analysis-based attack node selection leads to 3−11% higher degradation in DR compared to a random selection; (b) Stochastic geometry-based graph generation to create surrogate adversarial topologies and samples leads to 3−13% higher degradation in DR compared to traditional FDIAs; (c) Adopting an unsupervised spatio-temporal graph autoencoder (STGAE)-based detector enhances the DR by 5 − 53% compared to benchmark detectors against FDIEAs.