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A Research of Power Analysis Based on Multiple Neural Network Structures

机译：基于多神经网络结构的功率分析研究

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摘要

In order to explore the performance difference of different deep neural networks used in power analysis attacks, DPA_Contest_V4 dataset is used to complete the experiment. After cracking the loop mask, first we compare the deep neural network with the traditional machine learning algorithm model such as SVM. Then we analyze the influence of the changes in the structure of the neural network model on the experimental results. Finally, combined with the recurrent neural network, different network models are compared comprehensively. The result shows that under the same experimental conditions, the neural network model is superior to the traditional machine learning model and the recurrent neural network model is superior to the deep neural network model, in which different layers of neural networks taking different activation functions lead to large changes.
机译：为了探索用于功率分析攻击的不同深度神经网络的性能差异，使用DPA_Contest_V4数据集来完成实验。破解完循环掩码后，首先我们将深度神经网络与传统的机器学习算法模型（例如SVM）进行比较。然后，我们分析了神经网络模型结构变化对实验结果的影响。最后，结合递归神经网络，对不同的网络模型进行了综合比较。结果表明，在相同的实验条件下，神经网络模型优于传统的机器学习模型，而递归神经网络模型则优于深度神经网络模型。很大的变化。

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