Abstract: Due to the large sample size requirement for traditional Differential Power Analysis（DPA） methods and the problem of high computational resource consumption and long training cycles for the deep learning-based side-channel template attack, the implementation principle of convolutional neural networks is introduced. With the technical characteristics and theoretical analysis of the traditional differential energy analysis method in the actual attack, there is a Ghost peaks phenomenon and the appropriate data classification rules are selected. Based on the convolutional neural network, a new method of differential attack is proposed for encryption chips. By performing differential analysis and comparison experiments on the microcontroller running the DES encryption algorithm（AT89C52）, the experimental results show that the new method has greatly improved on the sample size requirements, and the new method does not need to continuously increase the number of iterations. Meanwhile, the new method has been optimized in terms of computing resource consumption and training cycle.

Key words: differential analysis, convolutional neural network, DES encryption algorithm, side-channel attack

摘要： 针对传统的差分能量分析（DPA）方法存在的样本规模需求较大以及基于深度学习的旁路模板攻击在计算资源消耗较高，训练周期较长等问题，在介绍了卷积神经网络的实现原理与技术特点、理论分析了传统的差分能量分析方法的实现过程以及选择了合适的数据类别划分规则的基础上，提出了一种基于卷积神经网络的加密芯片差分攻击新方法。通过对运行DES加密算法的微控制器（AT89C52）进行差分分析对比实验，实验结果表明，新方法较传统的差分方法在样本规模需求方面有较大的改善，并且新方法不需要不断地通过加大迭代次数来提高正确匹配率，在计算资源消耗和训练周期方面有所优化。

关键词: 差分分析, 卷积神经网络, DES加密算法, 旁路攻击