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Effective detection of energy theft can prevent revenue losses of utilitycompanies and is also important for smart grid security. In recent years,enabled by the massive fine-grained smart meter data, deep learning (DL)approaches are becoming popular in the literature to detect energy theft in theadvanced metering infrastructure (AMI). However, as neural networks are shownto be vulnerable to adversarial examples, the security of the DL models is ofconcern.  In this work, we study the vulnerabilities of DL-based energy theft detectionthrough adversarial attacks, including single-step attacks and iterativeattacks. From the attacker's point of view, we design the\textit{SearchFromFree} framework that consists of 1) a randomly adversarialmeasurement initialization approach to maximize the stolen profit and 2) astep-size searching scheme to increase the performance of black-box iterativeattacks. The evaluation based on three types of neural networks shows that theadversarial attacker can report extremely low consumption measurements to theutility without being detected by the DL models. We finally discuss thepotential defense mechanisms against adversarial attacks in energy theftdetection.

Exploiting Vulnerabilities of Deep Learning-based Energy Theft Detection in AMI through Adversarial Attacks