Search for: All records

Electricity theft is a type of cyberattack posing significant risks to the security of smart grids. Semi-supervised outlier detection (SSOD) algorithms utilize normal power usage data to build detection models, enabling them to detect unknown electricity theft attacks. In this paper, we applied feature engineering and ensemble learning to improve the detection performance of SSOD algorithms. Specifically, we extracted 22 time-series and wavelet features from load profiles, which served as inputs for the seven popular SSOD algorithms investigated in this study. Experimental results demonstrate that the proposed feature engineering greatly enhances the performance of SSOD algorithms to detect various false data injection (FDI) attacks. Furthermore, we constructed bagged ensemble models using the best-performing SSOD algorithm as the base model, with results indicating further improvements in detection performance compared to the base model alone.