A Covert Electricity-Theft Cyberattack Against Machine Learning-Based Detection Models

Abstract

The advanced metering infrastructure in modern networked smart homes brings various advantages, such as multiple pricing and energy scheduling. However, smart homes are vulnerable to many cyberattacks, and the most striking one is energy theft. Researchers have developed many countermeasures, fostered by advanced machine learning (ML) techniques. Nevertheless, recent advances are not robust enough in practice, partially due to the vulnerabilities of employed ML algorithms. Given a group of smart homes, in this article, we present a covert electricity theft strategy through mimicking normal consumption patterns and compromising neighboring meters concurrently. Such attack is almost impossible to be detected by existing solutions as the manipulated data have little deviation against honest usage records. To address this threat, we initially identify and define two levels of consumption deviations: home-level and interpersonal-level, respectively. Then, we design a feature extraction scheme that can capture the correlation between attacks and honest customers. Finally, we develop a new deep learning-based detection model. Extensive experiments based on real-world datasets show that the presented attack could evade existing mainstream detectors but still gain high profits. In addition, the proposed countermeasure outperforms state-of-the-art detection methods.