On Hiddenness of Moving Target Defense against False Data Injection Attacks on Power Grid

Abstract

Recent studies have exploited moving target defense (MTD) for thwarting false data injection (FDI) attacks against the state estimation (SE) by actively perturbing branch parameters (i.e., impedance or admittance) in power grids. To hide the activation of MTD from attackers, a new strategy named hidden MTD has been proposed by the latest literature. A hidden MTD can increase the defender’s chance to detect FDI attacks and avoid the attacker from inferring new branch parameters. However, by using an MTD-confirming detector like the bad data detection (BDD) checker in SE, we observe that it is still possible for the attacker to detect this hidden MTD when the power flows change with time. To uncover the insight of MTD’s hiddenness, we study the conditions needed for achieving a hidable MTD. We find that the hiddenness of MTD is closely related to the branch perturbations, system topology, and attacker’s knowledge. From the attacker’s perspective, we prove that an MTD can be detected by the attacker only if he/she knows the previous parameters of a set of branches that forms a circle and the measurements corresponding to those branches after MTD. But once the attacker has full knowledge of branch parameters before MTD and has obtained all measurements after MTD, it is proved that we can never achieve a hidable and effective MTD. From the defender’s perspective, since it is impossible to know the attacker’s capability, we cannot determine whether a constructed MTD is hidable or not by purely depending on the MTD design. To address this issue, we propose that, by protecting a basic set of measurements, we always can achieve a hidable and effective MTD regardless of the changes of power flows, the attacker’s knowledge, and the branch perturbations. Furthermore, we validate our findings with the IEEE standard test power systems.