Abnormal traffic-indexed state estimation: A cyber-physical fusion approach for Smart Grid attack detection

Integration with information network not only facilitates Smart Grid with many unprecedented features, but also introduces many new security issues, such as false data injection and system intrusion. One of the biggest challenges in Smart Grid attack detection is how to fuse the heterogeneous data from the power system and information network. In this paper, a novel cyber-physical fusion approach is proposed to detect a Smart Grid attack Bad Data Injection (BDI), by merging both the features of the traffic flow in information network and the inherent physical laws in the power system into a unified model, named as Abnormal Traffic-indexed State Estimation (ATSE). The cyber security incidents, monitored by intrusion detection system (IDS), are quantized to serve as the impact factors that are incorporated into the bad data detection system based on state estimation model in power grid. Hundreds of attack cases are simulated on each transmission line of three IEEE standard systems to compare ATSE with current cyber, physical abnormal detection methods and cyber-physical fusion method, including IDS (Snort), bad data detection algorithm (Chi-square test) and SCPSE. The results indicate that ATSE can improve the detection rate 20% than the Chi-square Test on average, filter most false alarms generated by Snort, and solve the observability problem of SCPSE.