Smart metering networks are increasingly susceptible to cyber threats, where false data injection (FDI) appears as a critical attack. Data-driven-based machine learning (ML) methods have shown immense benefits in detecting FDI attacks via data learning and prediction abilities. Literature works have mostly focused on centralized learning and deploying FDI attack detection models at the control center, which requires data collection from local utilities like meters and transformers. However, this data sharing may raise privacy concerns due to the potential disclosure of household information like energy usage patterns. This paper proposes a new privacy-preserved FDI attack detection by developing an efficient federated learning (FL) framework in the smart meter network with edge computing. Distributed edge servers located at the network edge run an ML-based FDI attack detection model and share the trained model with the grid operator, aiming to build a strong FDI attack detection model without data sharing. Simulation results demonstrate the efficiency of our proposed FL method over the conventional method without collaboration.

翻译：智能计量网络日益面临网络威胁，其中虚假数据注入攻击已成为关键性安全威胁。基于数据驱动的机器学习方法凭借其数据学习与预测能力，在检测虚假数据注入攻击方面展现出显著优势。现有研究主要集中于集中式学习模式，并将攻击检测模型部署于控制中心，这需要从电表、变压器等本地设备采集数据。然而，此类数据共享可能因泄露家庭用电模式等敏感信息而引发隐私担忧。本文通过在边缘计算智能计量网络中构建高效的联邦学习框架，提出了一种新型隐私保护型虚假数据注入攻击检测方案。部署在网络边缘的分布式边缘服务器运行基于机器学习的攻击检测模型，并将训练后的模型共享给电网运营商，从而在不共享原始数据的前提下构建强大的攻击检测模型。仿真实验表明，相较于传统非协作方法，我们提出的联邦学习方法具有更优的检测效能。