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, are becoming popular in the research community and shown to achieve state-of-the-art detection performance. However, in this work, we demonstrate that the well-trained ML models for energy theft detection are highly vulnerable to adversarial attacks. In particular, we design an adversarial measurement generation approach that enables the attacker to report extremely low power consumption measurements to 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 results demonstrate that our approach is able to significantly decrease the ML models' detection accuracy, even for black-box attackers.

本文介绍了基于神经网络技术的能量盗窃检测方法，以及如何使用对抗性攻击使得机器学习模型容易受到攻击，从而欺诈性报告极低的功率消耗测量值，绕过机器学习能量盗窃检测，实现攻击目的。实验结果表明，这种方法能够显著降低机器学习模型的检测准确性，即使对于黑盒攻击。

SearchFromFree: 机器学习基于的对抗测量用于能量窃取检测