光通信研究Issue(3)：45-51,7.DOI:10.13756/j.gtxyj.2024.230125
基于CNN-CBAM的虚假数据注入攻击辨识研究
Research on False Data Injection Attack Identification based on CNN-CBAM
摘要
Abstract
[Objective]It is always difficult to timely locate the location of the network attack and achieve rapid deployment of de-fense strategies when the smart grid is attacked by the network.[Methods]In order to solve this problem,this article proposes a Convolutional Neural Network(CNN)model that integrates Convolutional Block Attention Modules(CBAM)(CNN-CBAM)to detect False Data Injection Attack(FDIA)positions.The attack identification problem of FDIA is modeled as a multi label classification problem,where CNN is used to extract spatial features of the data.The CBAM module can be directly integrated in-to the convolution operation of the CNN module,which not only focuses on important parameter information from the perspective of spatial domain,but also considers feature relationships in the channel domain,and allocates attention to the input data from two dimensions to improve the performance of the model.[Results]The performance of the proposed CNN-CBAM network FDIA position detection model is verified on Institute of Electrical and Electronics Engineers(IEEE)14 and IEEE118 node systems.The experimental results show that the FDIA position detection rates of CNN-CBAM on IEEE14 and IEEE118 node systems are 98.25%and 96.72%,respectively.[Conclusion]Compared with other methods,the CNN-CBAM network model proposed in this paper can effectively extract the spatiotemporal characteristics between data,with improved existence of FDIA.It also im-proves the accuracy of attack location identification with better robustness.关键词
智能电网/虚假数据注入攻击/卷积注意力模块/卷积神经网络Key words
smart grid/FDIA/CBAM/CNN分类
信息技术与安全科学引用本文复制引用
周先军,王茹,刘航,金波..基于CNN-CBAM的虚假数据注入攻击辨识研究[J].光通信研究,2024,(3):45-51,7.基金项目
国家自然科学基金资助项目(61901165,61601177) （61901165,61601177）
湖北省自然科学基金资助项目(2019CFB530) （2019CFB530）
