Deep learning for online AC False Data Injection Attack detection in smart grids: An approach using LSTM-Autoencoder

Abstract The Power system is a crucial Cyber-Physical system and is prone to the False Data Injection Attack (FDIA). The existing FDIA detection mechanism focuses on DC state estimation. In this paper, we propose a phased AC FDIA targeting at generation rescheduling and load shedding. After injecting the false data into the measurements, the estimated states will be deviated from those in normal conditions. The proposed mechanism extracts the spatial and spectral features of the modes decomposed from the estimated states using variational mode decomposition (VMD). Then LSTM-Autoencoder is trained by learning the temporal correlations between the multi-dimensional feature vectors. The reconstruction error deviation vectors of the feature vectors are calculated and updated by LSTM-Autoencoder. Based on these error deviation vectors, the Logistic Regression (LR) classifier is trained to determine whether the error deviation vector is abnormal. We evaluate the performance of the proposed mechanism with comprehensive simulations on IEEE 14 and 118-bus systems. The results indicate that the mechanism can achieve a satisfactory attack detection accuracy.