Alshehri Ali, Badr Mahmoud M, Baza Mohamed, Alshahrani Hani
Department of Computer Science, University of Tabuk, Tabuk 71491, Saudi Arabia.
Department of Network and Computer Security, College of Engineering, SUNY Polytechnic Institute, Utica, NY 13502, USA.
Sensors (Basel). 2024 May 20;24(10):3236. doi: 10.3390/s24103236.
Smart power grids suffer from electricity theft cyber-attacks, where malicious consumers compromise their smart meters (SMs) to downscale the reported electricity consumption readings. This problem costs electric utility companies worldwide considerable financial burdens and threatens power grid stability. Therefore, several machine learning (ML)-based solutions have been proposed to detect electricity theft; however, they have limitations. First, most existing works employ supervised learning that requires the availability of labeled datasets of benign and malicious electricity usage samples. Unfortunately, this approach is not practical due to the scarcity of real malicious electricity usage samples. Moreover, training a supervised detector on specific cyberattack scenarios results in a robust detector against those attacks, but it might fail to detect new attack scenarios. Second, although a few works investigated anomaly detectors for electricity theft, none of the existing works addressed consumers' privacy. To address these limitations, in this paper, we propose a comprehensive federated learning (FL)-based deep anomaly detection framework tailored for practical, reliable, and privacy-preserving energy theft detection. In our proposed framework, consumers train local deep autoencoder-based detectors on their private electricity usage data and only share their trained detectors' parameters with an EUC aggregation server to iteratively build a global anomaly detector. Our extensive experimental results not only demonstrate the superior performance of our anomaly detector compared to the supervised detectors but also the capability of our proposed FL-based anomaly detector to accurately detect zero-day attacks of electricity theft while preserving consumers' privacy.
智能电网遭受电力盗窃网络攻击，恶意用户会破坏其智能电表（SM）以降低上报的用电量读数。这个问题给全球的电力公司带来了相当大的财务负担，并威胁到电网的稳定性。因此，已经提出了几种基于机器学习（ML）的解决方案来检测电力盗窃；然而，它们存在局限性。首先，大多数现有工作采用监督学习，这需要良性和恶意用电样本的标记数据集。不幸的是，由于真实恶意用电样本的稀缺，这种方法并不实用。此外，在特定网络攻击场景下训练监督检测器会得到针对这些攻击的鲁棒检测器，但它可能无法检测到新的攻击场景。其次，尽管有一些工作研究了电力盗窃的异常检测器，但现有工作都没有解决用户隐私问题。为了解决这些局限性，在本文中，我们提出了一个全面的基于联邦学习（FL）的深度异常检测框架，专为实用、可靠且保护隐私的能源盗窃检测量身定制。在我们提出的框架中，用户在其私人用电数据上训练基于深度自动编码器的本地检测器，并且仅与一个EUC聚合服务器共享其训练好的检测器参数，以迭代构建全局异常检测器。我们广泛的实验结果不仅证明了我们的异常检测器相对于监督检测器的优越性能，还证明了我们提出的基于FL的异常检测器在保护用户隐私的同时准确检测电力盗窃零日攻击的能力。
