Distributed Data-Driven Intrusion Detection for Sparse Stealthy FDI Attacks in Smart Grids

Abstract

The stealthy false data injection (FDI) attacks in smart grids can bypass the bad data detection, and thus make an incorrect state estimate in the control center. In this brief, a distributed data-driven intrusion detection approach is proposed to reveal the existence of the sparse stealthy FDI attack in a multi-area interconnected power system. The proposed distributed intrusion detection approach avoids the over-fitting issue that is extensively seen when implementing machine learning algorithms for large-scale systems. Firstly, each area estimates the entire system state based on a distributed state estimation algorithm. Then, the state of each local area is used as trained neural network input to detect the stealthy FDI attacks. Simulation results on the IEEE 118-bus system verify that the proposed method not only reduces the risk of over-fitting, but also can locate the areas which have been attacked.