Graph Neural Networks Based Detection of Stealth False Data Injection Attacks in Smart Grids
Graph Neural Networks Based Detection of Stealth False Data Injection Attacks in Smart Grids
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DOI：
10.1109/jsyst.2021.3109082
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发表时间：
2021-10-19
影响因子：
4.4
通讯作者：
Serpedin, Erchin
中科院分区：
文献类型：
--
作者：
Boyaci, Osman;Umunnakwe, Amarachi;Serpedin, Erchin
虚假数据注入攻击（FDIA）是一种主要的攻击类型，其目的是通过向电网中的智能计量设备注入虚假数据来破坏测量的完整性。据作者所知，没有研究试图设计一个检测器，自动建模的基础图形拓扑结构和空间相关的测量数据的智能电网，以更好地检测网络攻击。本文对检测和缓解FDIA的贡献是双重的。首先，我们提出了一个通用的，本地化的和隐形的（不可观察的）攻击生成方法和公开访问的数据集，供研究人员开发和测试他们的算法。其次，我们提出了一个基于图神经网络（GNN）的，可扩展的和实时的FDIA检测器，有效地结合了模型驱动和数据驱动的方法，通过将现代交流电网的固有物理连接和利用测量的空间相关性。通过比较所提出的基于GNN的检测器与文献中现有的FDIA检测器，实验验证了我们的算法在14，118和300总线的标准IEEE测试床的$F$1得分中分别优于最佳解决方案3.14%，4.25%和4.41%。
False data injection attacks (FDIAs) represent a major class of attacks that aim to break the integrity of measurements by injecting false data into the smart metering devices in power grids. To the best of authors' knowledge, no study has attempted to design a detector that automatically models the underlying graph topology and spatially correlated measurement data of the smart grids to better detect cyber attacks. The contributions of this article to detect and mitigate FDIAs are twofold. First, we present a generic, localized, and stealth (unobservable) attack generation methodology and publicly accessible datasets for researchers to develop and test their algorithms. Second, we propose a graph neural network (GNN) based, scalable and real-time detector of FDIAs that efficiently combines model-driven and data-driven approaches by incorporating the inherent physical connections of modern ac power grids and exploiting the spatial correlations of the measurement. It is experimentally verified by comparing the proposed GNN-based detector with the currently available FDIA detectors in the literature that our algorithm outperforms the best available solutions by 3.14%, 4.25%, and 4.41% in $F$1 score for standard IEEE testbeds with 14, 118, and 300 buses, respectively.