Robust Moving Target Defence Against False Data Injection Attacks in Power Grids

Recently, moving target defence (MTD) has been proposed to thwart false data injection (FDI) attacks in power system state estimation by proactively triggering the distributed flexible AC transmission system (D-FACTS) devices. One of the key challenges for MTD in power grid is to design its real-time implementation with performance guarantees against unknown attacks. Converting from the noiseless assumptions in the literature, this paper investigates the MTD design problem in a noisy environment and proposes, for the first time, the concept of robust MTD to guarantee the worst-case detection rate against all unknown attacks. We theoretically prove that, for any given MTD strategy, the minimal principal angle between the Jacobian subspaces corresponds to the worst-case performance against all potential attacks. Based on this finding, robust MTD algorithms are formulated for the systems with both complete and incomplete configurations. Extensive simulations using standard IEEE benchmark systems demonstrate the improved average and worst-case performances of the proposed robust MTD against state-of-the-art algorithms.

The Emerging implementation of information techniques has reformed the power gird into a complex cyber-physical power system (CPPS), where the two-way real-time communication among multiple parties raises new risks in the grid [1]. Musleh et al. [2] reviewed seven recent cyber attacks in energy industry and spotted the related vulnerabilities in both physical and cyber layers. Recently, false data injection (FDI) attacks against power system state estimation (SE) have been developed by intruding through the Modbus/TCP protocol without being noticed by the bad data detector (BDD) at the control centre [3], [4], [5], [6]. As accurate state estimation is crucial for energy management system (EMS) activities, such as generator dispatch, contingency analysis, and fault diagnosis, states falsified by FDI attacks can result in erroneous control actions, causing economic losses, system instability, and safety violation [7], [8], [9].