Locational Detection of the False Data Injection Attack in a Smart Grid: A Multilabel Classification Approach

Abstract

State estimation is critical to the monitoring and control of smart grids. Recently, the false data injection attack (FDIA) is emerging as a severe threat to state estimation. Conventional FDIA detection approaches are limited by their strong statistical knowledge assumptions, complexity, and hardware cost. Moreover, most of the current FDIA detection approaches focus on detecting the presence of FDIA, while the important information of the exact injection locations is not attainable. Inspired by the recent advances in deep learning, we propose a deep-learning-based locational detection architecture (DLLD) to detect the exact locations of FDIA in real time. The DLLD architecture concatenates a convolutional neural network (CNN) with a standard bad data detector (BDD). The BDD is used to remove the low-quality data. The followed CNN, as a multilabel classifier, is employed to capture the inconsistency and co-occurrence dependency in the power flow measurements due to the potential attacks. The proposed DLLD is “model-free” in the sense that it does not leverage any prior statistical assumptions. It is also “cost-friendly” in the sense that it does not alter the current BDD system and the runtime of the detection process is only hundreds of microseconds on a household computer. Through extensive experiments in the IEEE bus systems, we show that DLLD can perform locational detection precisely under various noise and attack conditions. In addition, we also demonstrate that the employed multilabel classification approach effectively enhances the presence-detection accuracy.