基于图注意力与多尺度并行融合卷积的虚假数据注入攻击定位检测  

Localization Detection for False Data Injection Attacks Based on Graph Attention and Multi-scale Parallel Fusion Convolution

作　　者：席磊 陈采玉[1] 陈洪军 李宗泽 XI Lei;CHEN Caiyu;CHEN Hongjun;LI Zongze(College of Electrical Engineering and New Energy,China Three Gorges University,Yichang 443002,China;Hubei Provincial Key Laboratory for Operation and Control of Cascaded Hydropower Station,China Three Gorges University,Yichang 443002,China)

机构地区：[1]三峡大学电气与新能源学院,宜昌443002 [2]梯级水电站运行与控制湖北省重点实验室(三峡大学),宜昌443002

出　　处：《高电压技术》2025年第4期1763-1772,共10页High Voltage Engineering

基　　金：国家自然科学基金(52277108)。

摘　　要：虚假数据注入攻击严重威胁电力信息物理系统的安全,而传统攻击检测方法由于没有考虑量测数据间的拓扑并且特征提取能力差,无法精确识别攻击并定位受攻击节点。因此,该文提出一种基于图注意力与多尺度并行融合卷积模型的虚假数据注入攻击定位检测方法。该方法通过图注意力网络动态捕捉量测数据间的拓扑关系以提升检测方法的定位检测性能;采用结合注意力特征融合模块增强的并行卷积神经网络提取数据的多尺度特征进一步提高检测方法的学习能力和泛化能力,以实现高精度的定位检测。通过在IEEE-14节点测试系统和IEEE-57节点测试系统中进行评估研究,与现有的定位检测方法相比,该文所提方法具有更优的F1值,分别高达98.40%、95.29%。因此,该方法能够更好地对虚假数据注入攻击进行定位检测。False data injection attacks pose a significant threat to the security of cyber-physical power system.Traditional attack detection methods fail to accurately identify and localize attacked nodes due to their lack of consideration for the topological relationships among measurement data and their inadequate feature extraction capabilities.Therefore,this paper introduces a novel detection method for locating false data injection attacks,based on a graph attention network and a multi-scale parallel fusion convolutional model.This method dynamically captures the topological relationships among measurement data through the graph attention network,enhancing the localization performance of the detection technique.It utilizes a parallel convolutional neural network,augmented with an attention feature fusion module,to extract multi-scale features,thereby improving the learning and generalization capabilities of the detection method to achieve high-precision localization.Evaluation studies conducted on the IEEE-14 and IEEE-57 bus test systems demonstrate that the proposed method precedes existing localization techniques,achieving F1 scores of 98.40%and 95.29%,respectively.Consequently,this method provides a more effective solution for the localization detection of false data injection attack.

关 键 词：虚假数据注入攻击 电力信息物理系统 图注意力网络 并行卷积 特征融合 

分 类 号：TM711[电气工程—电力系统及自动化]