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False data injection attacks (FDIAs) pose a significant threat to smart power grids. Recent efforts have focused on developing machine learning (ML) -based defense strategies against such attacks. However, existing strategies offer limited detection performance since they (a) lack the capability of embedding the spatial aspects of the power system topology in the detection mechanism, (b) offer topology-specific detection that does not generalize well to practical systems with seasonal reconfigurations in their topology, or (c) offer detection based on only seen types of FDIAs present in the training set. Therefore, in this paper, we aim to develop a defense strategy that offers an improved generalization ability and detection performance against unseen attacks. Towards this objective, we propose a graph autoencoder (GAE) -based detection strategy that (a) captures spatio-temporal features of power systems, hence, offering improved detection performance, (b) is trained on comprehensive graphs reflecting various realizations of power system topologies, hence, offering better generalization abilities, and (c) works effectively against unseen FDIAs. The proposed detector is trained and tested on various topological configurations from 14, 39, and 118-bus systems offering detection rates (DRs) of 93. 6%, 95. 7%, and 99. 1%, respectively, when tested against unseen FDIAs and unseen topologies. This presents an improvement of 11. 5 - 30\% compared to existing ML-based strategies.
Takiddin et al. (Fri,) studied this question.