Analysis of Moving Target Defense Against False Data Injection Attacks on Power Grid

Abstract

Recent studies have considered thwarting false data injection (FDI) attacks against state estimation in power grids by proactively perturbing branch susceptances. This approach is known as moving target defense (MTD). However, despite of the deployment of MTD, it is still possible for the attacker to launch stealthy FDI attacks generated with former branch susceptances. In this paper, we prove that, an MTD has the capability to thwart all FDI attacks constructed with former branch susceptances only if (i) the number of branches l in the power system is not less than twice that of the system states n (i.e., l ≥ 2n, where n + 1 is the number of buses); (ii) the susceptances of more than n branches, which cover all buses, are perturbed. Moreover, we prove that the state variable of a bus that is only connected by a single branch (no matter it is perturbed or not) can always be modified by the attacker. Nevertheless, in order to reduce the attack opportunities of potential attackers, we first exploit the impact of the susceptance perturbation magnitude on the dimension of the stealthy attack space, in which the attack vector is constructed with former branch susceptances. Then, we propose that, by perturbing an appropriate set of branches, we can minimize the dimension of the stealthy attack space and maximize the number of covered buses. Besides, we consider the increasing operation cost caused by the activation of MTD. Finally, we conduct extensive simulations to illustrate our findings with IEEE standard test power systems.

Smart Grid Cyber-Physical Attack and Defense: A Review

“…However, the detection effectiveness of MTDs under these two placements is not considered. Max-rank placement [129], [130] can make MTDs achieve the maximum rank of the composite matrix ( i.e., max-rank MTDs), a metric of the detection effectiveness. Spanning-tree placement proposed in [131] installs D-FACTS devices on the lines which form a spanning tree of the system.…”

“…Thus, these two methods must be constructed in the D-FACTS placements, which ensure the detection effectiveness, such as the max-rank placements. Second, the optimization-based operation takes both the economic cost and the detection effectiveness into account, in which the metric of detection effectiveness is maximized or taken as constraints [128], [130]. Finally, the hidden MTD operation method delicately selects D-FACTS setpoints such that all measurements remain the same after MTD is applied [134]- [136].…”

“…In this case, vigilant attackers cannot detect the MTD in place using BDD. To find suitable placement for hidden MTD operation, authors in [135] enumerate all placement combinations, while authirs in [136] use the maxrank placement in [130], with the help of protected meters.…”

Stealthy MTD Against Unsupervised Learning-Based Blind FDI Attacks in Power Systems

“…Under AC state estimation, system measurements will consist of real power flows defined by (2) and reactive power by (19)…”

“…In particular, MTD is proposed through either transmission switching [16] or admittance perturbation via distributed flexible AC transmissions (D-FACTS) devices [17][18] to change physical system topology to proactively drive BDD. An analysis of MTD against FDI attacks is offered in [19] where they prove the susceptibility of isolated state measurements and design an algorithm for branch perturbation selection. Some limitations of MTD were explored in [20].…”