Revealing a New Vulnerability of Distributed State Estimation: A Data Integrity Attack and an Unsupervised Detection Algorithm

This paper proposes a distributed false data injection attack (FDIA) by attacking to the boundary buses in an interconnected power system. The proposed attack utilizes the measurements corresponding to a set of boundary buses in each neighboring areas to inject arbitrary errors to the estimated states of those buses. It is demonstrated that the attack not only gets through the robust distributed estimators but also bypasses the convergence-based detection methods. Furthermore, in an illustrative example, the differences in the attack with the conventional FDIA are briefly explained. Then, finding the optimal attack vector to minimize the maximum difference between the per area errors by considering the attacker's limitations is formulated as a mixed-integer second-order cone programming (MISOCP) problem. Finally, an unsupervised machine learning-based detection method is proposed utilizing a kernel density estimation technique along with statistical measures. This follows an outlier detection to filter out attacks. To show the performance of the detector, the <formula><tex>$n-1$</tex></formula> contingency, which changes the probability distribution of data is analyzed. The proposed attack and