  Energy theft causes large economic losses to utility companies around the
world. In recent years, energy theft detection approaches based on machine
learning (ML) techniques, especially neural networks, become popular in the
research literature and achieve state-of-the-art detection performance.
However, in this work, we demonstrate that the well-perform ML models for
energy theft detection are highly vulnerable to adversarial attacks. In
particular, we design an adversarial measurement generation algorithm that
enables the attacker to report extremely low power consumption measurements to
the utilities while bypassing the ML energy theft detection. We evaluate our
approach with three kinds of neural networks based on a real-world smart meter
dataset. The evaluation result demonstrates that our approach can significantly
decrease the ML models' detection accuracy, even for black-box attackers.
