Super-Resolution Perception Assisted Spatiotemporal Graph Deep Learning Against False Data Injection Attacks in Smart Grid

Abstract

Developing the deep learning (DL) technique is a promising way to enhance smart grid (SG) cybersecurity. However, previous DL methods require massive attack samples for cyberattack correlation learning, whilst the real-world SG is incapable of providing such a large dataset. Moreover, existing work commonly focuses on extracting temporal features from power grid data for cyberattack detection, while the spatial features are insufficiently investigated. To address these limitations, a spatiotemporal graph deep learning (STGDL)-based scheme is proposed to detect cyberattacks without requiring attack samples. First, the graph convolution and temporal gated convolution are orchestrated to extract spatiotemporal features jointly. Then, a quantile regression training strategy is adopted to give normally operational bounds of state variables in state estimation (SE). It gets rid of limitations on needing attack samples, and the state bounds can indicate cyberattack anomalies. At last, a super-resolution perception (SRP) network is proposed. The SRP network is able to reconstruct the high-frequent data of estimated states from low-frequent SE results, so as to improve the temporal learning ability in the STGDL model. The feasibility and effectiveness of the proposed scheme are validated by conducting comprehensive and extensive experiments on the IEEE 30-bus and 118-bus benchmarks.