Detection of Electricity Theft False Data Injection Attacks in Smart Grids

Abstract

Malicious customers hack into their smart meters to reduce their electricity bills using various cyberattack types. Such actions lead to financial losses and stability issues in the power grid. Existing research on machine learning-based detection offers promising detection performance. However, such detectors have been tested on a single type of cyberattacks and report performance accordingly, which is not a realistic setup since malicious customers may inject different types of cyberattacks. In this work, we examine the robustness of state-of-the-art machine learning-based electricity theft detectors against a combination of false data injection attacks (FDIAs). Specifically, we inject traditional, evasion, and data poisoning attacks with low, medium, and high injection levels then report the detection performance. Our results show that sequential ensemble learning-based detection offers the most stable detection performance that degrades only by 5.3% when subject to high injection levels of FDIAs compared to 15.7–18.5% degradation rates for the stand-alone detectors.