论文标题
免费搜索：基于机器学习的能量盗窃检测的对抗测量
SearchFromFree: Adversarial Measurements for Machine Learning-based Energy Theft Detection
论文作者
论文摘要
能源盗窃给全球公用事业公司造成了巨大的经济损失。近年来，基于机器学习（ML）技术，尤其是神经网络的能量盗窃检测方法在研究文献中变得很流行，并实现了最新的检测性能。但是，在这项工作中，我们证明了良好的能源盗窃检测模型非常容易受到对抗攻击的影响。特别是，我们设计了一种对抗性测量产生算法，该算法使攻击者能够向公用事业报告极低的功耗测量，同时绕过ML能量盗窃检测。我们使用基于现实世界智能电表数据集的三种神经网络评估我们的方法。评估结果表明，即使对于黑盒攻击者，我们的方法也可以显着降低ML模型的检测准确性。
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.
