Detecting Stealthy False Data Injection Attacks in Power Grids Using Deep Learning

Abstract

The electric power grid, as a critical national infrastructure, is under constant threat from cyber-attacks. State estimation (SE) is at the foundation of a series of critical control processes in a power transmission system. A false data injection (FDI) attack against SE can disrupt these control processes, crippling a power system and wreaking havoc in a region. With knowledge of the system topology, a cyber-attacker can formulate and execute stealthy FDI attacks that are very difficult to detect. Statistical and, more recently, machine learning approaches have been undertaken to detect FDI attacks on SE of the power grid. In this paper, we propose a Deep Learning (DL) based method to accurately detect stealthy FDI attacks on the SE of power grid. We compare the performance of the DL method with three popular machine learning algorithms, which are: gradient boosting machines (GBM), generalized linear modelings (GLM) and distributed random forests (DRF). All four algorithms analyze a dataset simulating the IEEE 14-bus system. The results demonstrate that these algorithms perform well in accurately and precisely detecting stealthy FDI attacks on the smart grid, with the DL-based approach showing best results.