Relentless False Data Injection Attacks Against Kalman-Filter-Based Detection in Smart Grid
Yifa Liu, Long Cheng
Abstract
As one of the most dangerous cyber attacks in smart grids, the false data injection attacks pose a serious threat to power system security. To detect the false data, the traditional residual method and other improved methods, such as the Kalman-filter-based detector, have been proposed. However, these methods often have defects, especially in a very complex networked system with noises. By investigating the tolerance to the uncertainty in the residual detection method and properties of noises, the attack magnitude planning has been presented to hide the attack behind noises, which can bypass the residual detection method. As to the Kalman-filter-based detector, this article designs a specific attack strategy that can successfully deceive the Kalman-filter-based detector. Under this strategy, the false data injected at each step are used to balance the anomalies caused by previous false data, making the system look quite normal in monitoring, while deviating the system from normal operation eventually.