Analysis of moving target defense against false data injection attacks on power grid

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 \geq 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.Agency for Science, Technology and Research (A*STAR)Nanyang Technological UniversityThis work was supported in part by the National Key Research and Development Program of China under Grant 2016YFB0800204, in part by the National Natural Science Foundation of China under Grant 61833015, in part by the Singapore University of Technology and Design-Zhejiang University Innovation, Design and Entrepreneurship Alliance (SUTD-ZJU IDEA) under Award 201805, in part by the Nanyang Technological University (NTU) Internal Funding-Start-up Grant (SUG)- the College of Engineering (CoE) under Grant M4082287, and in part by the A*STAR- Nanyang Technological University-Singapore University of Technology and Design AI Partnership under Grant RGANS1906. This article was presented at the IEEE PES ISGT 2019 [1]. The associate editor coordinating the review of this manuscript and approving it for publication was Dr. Walid Saad