Exploiting Vulnerabilities of Deep Learning-based Energy Theft Detection in AMI through Adversarial Attacks
This research highlights a critical security vulnerability for utility companies relying on DL for energy theft detection, potentially leading to significant revenue losses.
This paper investigates the vulnerability of deep learning (DL) models used for energy theft detection in Advanced Metering Infrastructure (AMI) to adversarial attacks. The study demonstrates that an attacker can manipulate consumption measurements to appear extremely low without being detected by the DL models.
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.