False Data Injection Attack Detection Based on Redundant Sparse Reconstruction

As a well-known type of attack, the concept of false data injection attack (FDIA) was originated from electric power grids, and expanded to other fields such as healthcare, transportation, etc. Since the injections of FDIA are hidden in the real measurements, the attack is difficult to be detected. In this paper, we propose a universal detection method against FDIA, which does not need any auxiliary probability distribution model. In order to expose the change of sparsity arising from FDIA, this study first focuses on the structure of mapping matrix and makes it redundant. Column redundancy provides a necessary spatial condition and row redundancy provides a necessary measurement condition, for exposing the change of sparsity caused by FDIA. To prevent an attacker from inferring redundant information, the redundant matrix and system states are randomly scrambled by chaotic system. By the aid of the proposed redundant sparse reconstruction (RSR) algorithm, two different reconstructions of system states are then generated to detect FDIA. Compared with the existing detection methods, the proposed scheme can not only detect FDIA, but also remove it in some degree. Finally, the feasibility of the proposed scheme is verified in the simulations.