Localizing False Data Injection Attacks in Smart Grid: A Spectrum-Based Neural Network Approach

Abstract

Smart grid is confronted with cyberattacks due to the increasing dependence on cyberspace. False data injection attacks (FDIAs) represent a major type of cyberattacks that cannot be detected by the traditional bad data detection (BDD). The majority of existing researches focus on how to detect FDIAs, while little attention has been paid to obtaining the exact attack locations, which are crucial for deploying countermeasures in time. In this paper, we propose a neural network-based approach for online localizing FDIAs in AC power systems. The approach transforms the multi-dimensional time series measurements to their time-frequency and spectral graph representations, to jointly capture the temporal and spatial correlations in the spectral domain. The spectral decomposition could better characterize the latent properties of measurements, such as auto-correlations of a bus and key dependency between buses, which would be poorly represented in the time domain and vertex domain. Specifically, the proposed approach first adopts the short-time Fourier transform (STFT) and a two-channel convolutional neural network (2C-CNN) to model the spectral-temporal correlations. Then the spectral-spatial relationships are modeled with a graph convolutional network (GCN), which combines the nodal admittance matrix and physical property of power systems. The prior physical knowledge further improves our approach’s interpretability and localization performance. We evaluate the effectiveness of our proposed approach with comprehensive case studies on various standard test systems. Numerical results indicate the superiority of our approach over baselines.