Locational False Data Injection Attack Detection in Smart Grid Using Recursive Variational Graph Autoencoder

Stealthy false data injection attack (FDIA) that intentionally modifies measurement data of smart grid meters to bypass the traditional bad data detection module is one of menacing cyber attacks in smart grid. Due to requiring no costly labeling abnormal measurement data, deep neural networks (DNNs)-based unsupervised FDIA detection has attracted great attentions. However, the existing schemes have two weaknesses. First, most schemes did not take into account the inherent spatial relationships between measurements in the grid. Second, for practical usage, the robustness and generalization of the trained FDIA detection scheme will be influenced by potential noisy measurement data. To address the issues above, based on the spatial graph neural network (GNN) architecture, a novel FDIA detection and localization scheme is proposed, named as recursive variational graph autoencoder (ReVGAE). Specifically, our contributions are following. The variational graph autoencoder (VGAE) module in our proposed ReVGAE innovatively plays dual roles: 1) data and topology reconstructor and 2) the denoising module. The first role aims to simultaneously reconstruct both nodes’ temporal measurements and topological relationship between nodes. In the second role, the outputs of VGAE as the reconstructor (i.e., the reconstructed temporal measurements) are intentionally used as the artificially noisy samples, and recursively fed into VGAE as input to improve the model’s robustness. Then, the residual between the finally reconstructed and the observed measurement data on each node is viewed as anomaly score to judge whether FDIA temporally happens on each node. Thorough experiments on a real grid system demonstrate that the proposed ReVGAE outperforms other variational autoencoder and GNN-based FDIA anomaly detection schemes.