文献类型：期刊文献

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

机构：[1] Beijing Key Laboratory of Information Service Engineering, Beijing, 100101, China; [2] College of Robotics, Beijing Union University, Beijing, 100101, China; [3] Institute of Semiconductors, Chinese Academy of Sciences, Beijing, 100083, China; [4] Research Centre for Multi-intelligent Systems, Beijing, 100101, China

第一机构：Beijing Key Laboratory of Information Service Engineering, Beijing, 100101, China

年份：2022

卷号：1726 CCIS

起止页码：105-118

外文期刊名：Communications in Computer and Information Science

收录：EI(收录号：20230113332761)

语种：英文

外文关键词：Convolution - Convolutional neural networks - Deep learning - Energy efficiency - Network layers - Side channel attack

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. ? 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.