  Effective detection of energy theft can prevent revenue losses of utility
companies 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 the
advanced metering infrastructure (AMI). However, as neural networks are shown
to be vulnerable to adversarial examples, the security of the DL models is of
concern.
  In this work, we study the vulnerabilities of DL-based energy theft detection
through adversarial attacks, including single-step attacks and iterative
attacks. From the attacker's point of view, we design the
\textit{SearchFromFree} framework that consists of 1) a randomly adversarial
measurement initialization approach to maximize the stolen profit and 2) a
step-size searching scheme to increase the performance of black-box iterative
attacks. The evaluation based on three types of neural networks shows that the
adversarial attacker can report extremely low consumption measurements to the
utility without being detected by the DL models. We finally discuss the
potential defense mechanisms against adversarial attacks in energy theft
detection.
