基于GATv2模型的虚假数据注入攻击检测方法  

Detection of False Data Injection Attack Based on GATv2 Model

作　　者：罗小元[1] 耿艺帆 吴莉艳 王新宇[1,3] LUO Xiaoyuan;GENG Yifan;WU Liyan;WANG Xinyu(Institute of Electrical Engineering,Yanshan University,Qinhuangdao 066004;Xinfa Group Co.,Ltd.,Liaocheng 252100;Sichuan Provincial Key Laboratory of Smart Grid,Sichuan University,Chengdu 610095)

机构地区：[1]燕山大学电气工程学院,秦皇岛066004 [2]信发集团有限公司,聊城252100 [3]四川大学智能电网四川省重点实验室,成都610095

出　　处：《电气工程学报》2024年第3期353-361,共9页Journal of Electrical Engineering

基　　金：国家自然科学基金(62103357);河北省自然科学基金青年(F2021203043);智能电网四川省重点试验室(2023-IEPGKLSP-KFYB05)资助项目。

摘　　要：虚假数据注入攻击(False data injection attack,FDIA)能够躲避传统不良数据检测器,给智能电网的稳定运行带来了挑战。因此,提出了一种基于改进图注意力网络(Graph attention network v2,GATv2)模型的FDIA检测方法。首先,基于电力系统结构和FDIA的特性,构建模型所需数据集;然后,根据电力系统的拓扑信息和运行信息建立图数据;设计基于GATv2的检测模型对电网图数据的空间特征进行提取,进而检测注入的虚假数据攻击;最后,在IEEE 14节点和IEEE 118节点系统上验证了GATv2模型的有效性,且仿真结果表明GATv2模型检测性能优于其他模型,检测准确率达到98%以上,在不同攻击节点数和不同攻击强度情况中都具有较好的鲁棒性。False data injection attack(FDIA)can evade traditional bad data detectors and pose challenges to the stable operation of smart grids.Therefore,an FDIA detection method based on an improved graph attention network(GATv2)model is proposed.Firstly,based on the structure of the power system and the characteristics of FDIA,the required dataset is constructed for the model.Then,graph data is established based on the topology and operational information of the power system.A detection model is designed based on GATv2 to extract spatial features of power grid diagram data and detect injected false data attacks.Finally,the effectiveness of the GATv2 model is validated on IEEE 14 node and IEEE 118 node systems,and simulation results show that the detection performance of the GATv2 model is superior to other models,with a detection accuracy of over 98%,and it has good robustness in different attack node numbers and attack intensities.

关 键 词：智能电网 虚假数据注入攻击 深度学习 图注意力网络 不良数据检测 

分 类 号：TM561[电气工程—电器]