文献类型：会议论文

英文题名：Power Analysis Attack Based on Lightweight Convolutional Neural Network

作者：Li, Xiang[1];Yang, Ning[1];Chen, Aidong[2,4];Liu, Weifeng[3];Liu, Xiaoxiao[4];Huang, Na[2,4]

第一作者：Li, Xiang

通讯作者：Chen, AD[1];Chen, AD[2]

机构：[1]Beijing Key Lab Informat Serv Engn, Beijing 100101, Peoples R China;[2]Beijing Union Univ, Coll Robot, Beijing 100101, Peoples R China;[3]Chinese Acad Sci, Inst Semicond, Beijing 100083, Peoples R China;[4]Res Ctr Multiintelligent Syst, Beijing 100101, Peoples R China

第一机构：Beijing Key Lab Informat Serv Engn, Beijing 100101, Peoples R China

通讯机构：[1]corresponding author), Beijing Union Univ, Coll Robot, Beijing 100101, Peoples R China;[2]corresponding author), Res Ctr Multiintelligent Syst, Beijing 100101, Peoples R China.|[1141739]北京联合大学机器人学院;[11417]北京联合大学;

会议论文集：5th International Conference on Frontiers in Cyber Security (FCS)

会议日期：DEC 13-15, 2022

会议地点：Kumasi, GHANA

语种：英文

外文关键词：Side channel analysis; Power consumption attacks; Deep learning; Attention mechanisms

摘要：Since the beginning of the 21st century, modern information technology and electronic integrated circuit technology have developed rapidly. In the chip industry, the ability to resist side-channel attacks has become an important indicator for international mainstream evaluation agencies to evaluate chip security. This paper proposes an improved method for side channel analysis based on the CNNbest model, incorporating a lightweight combined channel and space convolutional attention module, optimising the position of the attention module, improving the learning efficiency of key features of the power consumption curve, and effectively reducing the number of traces used by the attack model. The addition of dropout layer network structure solves the problem that the model is prone to rapid overfitting. The optimal value of drop rate is sought through comparative experiments to speed up the convergence of the model and reduce the number of traces required for a successful attack. The experimental results show that the number of traces required by the method in this paper for side-channel attacks is reduced by 88% compared with the original model, which significantly improves the attack performance and can meet the requirements of side-channel modeling and analysis.