Statistical outlier detection for diagnosis of cyber attacks in power state estimation

Detection of stealthy attacks on alternating current (AC) static state estimation through false data injection is considered in this paper. To detect the presence of such cyber attacks, we follow a statistical outlier detection approach using a recently proposed machine learning technique called density ratio estimation. The proposed method offers an improved detection especially since our technique does not require attack models when compared to other machine learning techniques implemented in the smart grid literature such as the support vector machine. The simulations are conducted on the IEEE 118-bus system where multiple time scans are merged together to enhance the cyber-security for static state estimation.