Optimal malicious attack construction and robust detection in Smart Grid cyber security analysis

Malicious data injection attacks against smart grid systems are one of the most significant problems in cyber security. Previously, these are either targeted attacks or attacks with restrictions. In this paper, optimal random undetectable attacks are considered for the first time. In the state of the art, stealth attacks can always exist if the number of compromised measurements exceeds a certain value. In this paper, it is shown that undetectable attacks can be accomplished by altering only a much smaller number of measurements. On the other hand, a robust detection algorithm is designed by taking advantage of the sparse and low rank properties of the block measurements for a time interval. The element-wise constraints are introduced to improve the error tolerance and the problem of detecting false data with partial observations is also considered in this paper. The performance of the proposed algorithms is investigated based on IEEE test systems.