There has been a growing interest in the side-channel analysis (SCA) field based on deep learning (DL) technology. Various DL network or model has been developed to improve the efficiency of SCA. However, few studies have investigated the impact of the different models on attack results and the exact relationship between power consumption traces and intermediate values. Based on the convolutional neural network and the autoencoder, this paper proposes a Template Analysis Pre-trained DL Classification model named TAPDC which contains three sub-networks. The TAPDC model detects the periodicity of power trace, relating power to the intermediate values and mining the deeper features by the multi-layer convolutional net. We implement the TAPDC model and compare it with two classical models in a fair experiment. The evaluative results show that the TAPDC model with autoencoder and deep convolution feature extraction structure in SCA can more effectively extract information from power consumption trace. Also, Using the classifier layer, this model links power information to the probability of intermediate value. It completes the conversion from power trace to intermediate values and greatly improves the efficiency of the power attack.

基于深度学习 (DL) 技术的侧信道分析 (SCA) 领域越来越受到关注。已经开发了各种 DL 网络或模型来提高 SCA 的效率。然而，很少有研究调查不同模型对攻击结果的影响以及功耗轨迹与中间值之间的确切关系。基于卷积神经网络和自动编码器，本文提出了一种模板分析预训练的DL分类模型TAPDC，它包含三个子网络。TAPDC 模型检测功率轨迹的周期性，将功率与中间值相关联，并通过多层卷积网络挖掘更深层次的特征。我们实现了 TAPDC 模型，并在公平的实验中将其与两个经典模型进行了比较。评估结果表明，在 SCA 中具有自编码器和深度卷积特征提取结构的 TAPDC 模型可以更有效地从功耗轨迹中提取信息。此外，使用分类器层，该模型将功率信息与中间值的概率联系起来。完成了功率轨迹到中间值的转换，大大提高了功率攻击的效率。