With the growing concern about the security and privacy of smart grid systems, cyberattacks on critical power grid components, such as state estimation, have proven to be one of the top-priority cyber-related issues and have received significant attention in recent years. However, cyberattack detection in smart grids now faces new challenges, including privacy preservation and decentralized power zones with strategic data owners. To address these technical bottlenecks, this paper proposes a novel Federated Learning-based privacy-preserving and communication-efficient attack detection framework, known as FedDiSC, that enables Discrimination between power System disturbances and Cyberattacks. Specifically, we first propose a Federated Learning approach to enable Supervisory Control and Data Acquisition subsystems of decentralized power grid zones to collaboratively train an attack detection model without sharing sensitive power related data. Secondly, we put forward a representation learning-based Deep Auto-Encoder network to accurately detect power system and cybersecurity anomalies. Lastly, to adapt our proposed framework to the timeliness of real-world cyberattack detection in SGs, we leverage the use of a gradient privacy-preserving quantization scheme known as DP-SIGNSGD to improve its communication efficiency. Extensive simulations of the proposed framework on publicly available Industrial Control Systems datasets demonstrate that the proposed framework can achieve superior detection accuracy while preserving the privacy of sensitive power grid related information. Furthermore, we find that the gradient quantization scheme utilized improves communication efficiency by 40% when compared to a traditional federated learning approach without gradient quantization which suggests suitability in a real-world scenario.

翻译：随着智能电网系统安全与隐私问题日益受到关注，针对状态估计等关键电网组件的网络攻击已成为网络相关领域的首要问题之一，并在近年获得显著关注。然而，智能电网中的网络攻击检测如今面临新的挑战，包括隐私保护以及具有战略性数据所有权的分散式电力区域。为解决这些技术瓶颈，本文提出一种基于联邦学习的隐私保护且高通信效率的新型攻击检测框架——FedDiSC，能够区分电力系统扰动与网络攻击。具体而言，我们首先提出一种联邦学习方法，使分散式电网区域的监控与数据采集子系统能够在不共享敏感电力相关数据的情况下协作训练攻击检测模型。其次，我们提出一种基于表征学习的深度自编码器网络，以精确检测电力系统与网络安全异常。最后，为使所提框架适应智能电网中实时网络攻击检测的时效性，我们采用一种名为DP-SIGNSGD的梯度隐私保护量化方案，以提升其通信效率。在公开工业控制系统数据集上对所提框架的广泛仿真表明，该框架在保护敏感电网相关信息隐私的同时，能够实现优越的检测精度。此外，我们发现与未采用梯度量化的传统联邦学习方法相比，所采用的梯度量化方案可将通信效率提升40%，这表明其适用于真实场景。