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余玥琳, 武小年, 张润莲. 基于卷积残差网络的SM4算法分析[J]. 桂林电子科技大学学报, 2023, 43(1): 75-79.
引用本文: 余玥琳, 武小年, 张润莲. 基于卷积残差网络的SM4算法分析[J]. 桂林电子科技大学学报, 2023, 43(1): 75-79.
YU Yuelin, WU Xiaonian, ZHANG Runlian. Cryptanalysis of SM4 algorithm based on convolutional residual networks[J]. Journal of Guilin University of Electronic Technology, 2023, 43(1): 75-79.
Citation: YU Yuelin, WU Xiaonian, ZHANG Runlian. Cryptanalysis of SM4 algorithm based on convolutional residual networks[J]. Journal of Guilin University of Electronic Technology, 2023, 43(1): 75-79.

基于卷积残差网络的SM4算法分析

Cryptanalysis of SM4 algorithm based on convolutional residual networks

  • 摘要: 由于密码分析与深度学习之间天然的相似性,各种深度学习技术开始被应用于密码分析中。为分析国密SM4算法的安全性,采用卷积残差网络构建模型,搜索SM4算法差分区分器。模型基于选择的明文差值和数据集进行训练,通过数据处理、参数和函数的优化,构造了3~8轮差分区分器。测试结果表明,模型可以对SM4算法低轮数加密的密文对与随机数据进行区分,但随着轮数的增加,模型已无法有效区分密文对和随机数据。结果表明,SM4算法具有良好的安全性。

     

    Abstract: Due to the natural similarity between cryptanalysis and deep learning, various deep learning technologies have been applied to cryptanalysis. In order to analyze the security of state secret SM4 algorithm, the convolution residual network is used to build a model and search the difference divider of SM4 algorithm. The model is trained based on the selected plaintext difference and data set. Through data processing, parameter and function optimization, a 3-8 round difference divider is constructed. The model can distinguish the ciphertext pairs encrypted by SM4 algorithm with low number of rounds from random data, but with the increase of the number of rounds, the model can not effectively distinguish the ciphertext pair from the random data. Results show that SM4 algorithm has good security.

     

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