A two-stage attacking scheme for low-sparsity unobservable attacks in smart grid

False data injection attacks have serious threat to the smart grid, e.g., may incur power outage or blackout. Normally, an intruder should have priori knowledge of the linear structure matrix and then control all smart meters to perform attacks. State-of-the-art studies have proven in theory that false data injection attacks can be unobservable when an intruder coordinately controls a small number of smart meters. However, there are no practical or implementable unobservable false data injection attacks with low-sparsity yet in the literature. In this paper, we propose a two-stage attacking scheme to demonstrate the practical feasibility of unobservable false data injection attacks in the smart grid. In the first stage, we explore the parallel factor analysis to derive the linear structure matrix of the smart grid using the intercepted data. In the second stage, we construct the sparse attack vector via a linear-based relaxation approach, which is used as the false data. Results indicate that we can realize highly successful attacking performance with a low detection probability.