用联邦学习在不共享数据前提下检测智能电表网络的虚假数据攻击
False Data Injection Attack Detection in Edge-based Smart Metering Networks with Federated Learning
- 在边缘计算架构中部署联邦学习，各节点本地训练模型并只共享参数
- 仿真显示该方法比传统集中式方法检测效率更高
- 适合关注隐私保护的智能电网安全系统开发者
智能电表网络正面临日益严峻的网络威胁，其中虚假数据注入（FDI）攻击尤为关键。基于数据驱动的机器学习方法通过数据学习与预测能力，在检测FDI攻击方面展现出巨大潜力。现有研究多集中于中心化学习，将检测模型部署于控制中心，需从电表、变压器等本地设备收集数据，但此过程可能暴露家庭用电模式等隐私信息。本文提出一种新型隐私保护的FDI攻击检测机制，构建基于边缘计算的高效联邦学习（FL）框架。分布式边缘服务器在本地运行基于ML的FDI检测模型，并将训练后的模型参数共享给电网运营商，实现强检测模型构建而无需直接共享原始数据。仿真结果表明，所提联邦学习方法在无协作情形下具有更高的检测效率。
原文摘要 · Abstract (English)
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.
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