基于深度学习的类SM4算法S盒逆向分析

doi:10.13190/j.jbupt.2020-034

北京邮电大学学报 ›› 2020, Vol. 43 ›› Issue (5): 118-124.doi: 10.13190/j.jbupt.2020-034

基于深度学习的类SM4算法S盒逆向分析

马向亮^1,2, 李冰³, 杨丹³, 黄克振^1,2, 段晓毅⁴

1. 中国科学院软件研究所可信计算与信息保障实验室, 北京 100190;
2. 中国科学院大学, 北京 100049;
3. 国家信息技术安全研究中心, 北京 100084;
4. 北京电子科技学院电子信息工程系, 北京 100070

收稿日期:2020-03-31 发布日期:2021-03-11
通讯作者: 段晓毅(1979-),男,讲师,硕士生导师,E-mail:duanxiaoyi@besti.edu.cn. E-mail:duanxiaoyi@besti.edu.cn
作者简介:马向亮(1986-),男,博士生.
基金资助:
国家重点研发计划项目（2018YFB0904900，2018YFB0904901）；"十三五"装备预研领域基金项目（6140002020115）

Reverse-Analysis of S-Box for SM4-Like Algorithms Based on Side Channel Technology

MA Xiang-liang^1,2, LI Bing³, YANG Dan³, HUANG Ke-zhen^1,2, DUAN Xiao-yi⁴

1. Trusted Computing and Information Assurance Laboratory, Institute of Software of Chinese Academy of Sciences, Beijing 100190, China;
2. University of Chinese Academy of Sciences, Beijing 100049, China;
3. National Research Center for Information Technology Security, Beijing 100084, China;
4. Department of Electronics and Information Engineering, Beijing Electronic Science and Technology Institute, Beijing 100070, China

Received:2020-03-31 Published:2021-03-11

摘要/Abstract

摘要： 在建模类攻击场景下，基于多元高斯分布的模板攻击是常用的侧信道逆向分析方法.在同样的场景下，分析了深度学习方法在逆向分析领域的应用，提出了基于深度学习的S盒逆向分析算法.通过选取适用于侧信道逆向分析的深度学习算法、损失函数和标签设计，对类SM4算法进行了S盒逆向恢复实验.实验结果表明，使用深度学习进行S盒逆向分析是可行的，且在一定的条件下优于模板攻击；另外，多层感知机算法预测的结果要优于卷积神经网络算法预测的结果.

关键词: 深度学习, 逆向分析, S盒, 类SM4算法

Abstract: In the profiled scenario, the common method of reverse analysis is the template attack based on multi-Gaussian distribution. The article applies the concept of deep learning to the field of reverse analysis for the first time under the same conditions, and proposes an S-box reverse analysis algorithm based on deep learning. By selecting the deep learning algorithm, loss function and label design method suitable for side channel reverse analysis, an S-box reverse recovery experiment is conducted for SM4-like algorithm. It is shown that it is feasible to employ deep learning method to carry out S-box reverse analysis, which can have better performance comparing to template attack under certain circumstances. Moreover, the predicting effect of multi-layer perception algorithm surpasses that of convolutional neural network algorithm.

Key words: deep learning, reverse analysis, S box, SM4-like algorithm

中图分类号:

TP309.1

马向亮, 李冰, 杨丹, 黄克振, 段晓毅. 基于深度学习的类SM4算法S盒逆向分析[J]. 北京邮电大学学报, 2020, 43(5): 118-124.

MA Xiang-liang, LI Bing, YANG Dan, HUANG Ke-zhen, DUAN Xiao-yi. Reverse-Analysis of S-Box for SM4-Like Algorithms Based on Side Channel Technology[J]. Journal of Beijing University of Posts and Telecommunications, 2020, 43(5): 118-124.

参考文献

[1] Biryukov A, Shamir A. Structural cryptanalysis of SASAS[C]//International Conference on the Theory and Applications of Cryptographic Techniques. Heidelberg:Springer, 2001:395-405.
[2] Torrance R, James D. The state-of-the-art in IC reverse engineering[C]//International Workshop on Cryptographic Hardware and Embedded Systems. Heidelberg:Springer, 2009:363-381.
[3] Clavier C, Wurcker A. Reverse engineering of a secret AES-like cipher by ineffective fault analysis[C]//2013 Workshop on Fault Diagnosis and Tolerance in Cryptography. Piscataway:IEEE, 2013:119-128.
[4] Daudigny R, Ledig H, Muller F, et al. SCARE of the DES[C]//International Conference on Applied Cryptography and Network Security. Heidelberg:Springer, 2005:393-406.
[5] Novak R. Side-channel based reverse engineering of secret algorithms[C]//Proceedings of the 12th International Electro Technical and Computer Science Conference. Piscataway:IEEE, 2003:445-448.
[6] Rivain M, Roche T. SCARE of secret ciphers with SPN structures[C]//International Conference on the Theory and Application of Cryptology and Information Security. Heidelberg:Springer, 2013:526-544.
[7] Biryukov A, Perrin L. On reverse-engineering S-boxes with hidden design criteria or structure[C]//Annual Cryptology Conference. Heidelberg:Springer, 2015:116-140.
[8] Gao Si, Chen Hua, Wu Wenling, et al. My traces learn what you did in the dark:recovering secret signals without key guesses[C]//Cryptographers' Track at the RSA Conference.[S.l.]:Springer, 2017:363-378.
[9] 马向亮, 李冰, 习伟, 等. 基于独立分量技术的类GIFT算法S盒逆向分析[J]. 计算机研究与发展, 2018, 55(10):2269-2277. Ma Xiangliang, Li Bing, Xi Wei, et al. Reverse-analysis of S-box for GIFT-like algorithms based on independent component analysis technology[J]. Journal of Computer Research and Development, 2018, 55(10):2269-2277.
[10] Timon B. Non-profiled deep learning-based side-channel attacks with sensitivity analysis[J]. IACR Transactions on Cryptographic Hardware and Embedded Systems, 2019, 2019(2), 107-131.
[11] 张蕾, 吴文玲. SMS4密码算法的差分故障攻击[J]. 计算机学报, 2006, 29(9):1596-1602. Zhang Lei, Wu Wenling. Differential fault analysis on SMS4[J]. Chinese Journal of Computers, 2006, 29(9):1596-1602.
[12] 马向亮, 王宏, 李冰, 等. 基于能量分析技术的芯片后门指令分析方法[J]. 电子学报, 2019, 47(3):686-691. Ma Xiangliang, Wang Hong, Li Bing, et al. A power analysis method against backdoor instruction in chips[J]. Acta Electronica Sinica, 2019, 47(3):686-691.
[13] Maghrebi H, Portigliatti T, Prouff E. Breaking cryptographic implementations using deep learning techniques[C]//International Conference on Security, Privacy, and Applied Cryptography Engineering.[S.l.]:Springer, 2016:3-26.

[1]	刘阳, 滕颖蕾, 牛涛, 郅佳琳. 基于深度强化学习的滤波器剪枝方案[J]. 北京邮电大学学报, 2023, 46(3): 31-36.
[2]	靳梦凡, 黄智濒, 储志强. 复杂环境下多模态指导的点云补全方法[J]. 北京邮电大学学报, 2023, 46(3): 103-108.
[3]	荣震宇刘建毅. 基于Transformer和MLP的眼底血管分割算法[J]. 北京邮电大学学报, 2023, 46(1): 26-31.
[4]	刘金桐, 杨国兴, 刘晓鸿, 王光宇. 针对心电图分类模型的平滑攻击算法[J]. 北京邮电大学学报, 2022, 45(4): 44-50.
[5]	黄柳婷, 刘可欣, 牛凯, 常春, 贺志强. 基于深度学习的哮喘患者CT影像黏液栓自动识别[J]. 北京邮电大学学报, 2022, 45(4): 58-63.
[6]	柳长源, 虎浩媛, 毕晓君. 双线性融合网络的驾驶员分心行为识别[J]. 北京邮电大学学报, 2022, 45(2): 79-84.
[7]	赵海英, 朱会, 侯小刚. 基于改进EMA单元的传统服饰图像语义分割[J]. 北京邮电大学学报, 2022, 45(1): 69-74.
[8]	张滨宇, 赵衍运, 杜昀昊, 万俊峰, 佟知航. 一种基于深度学习的PCB图像字符检测方法[J]. 北京邮电大学学报, 2022, 45(1): 108-114.
[9]	贾珺, 冯春燕, 夏海轮, 张天魁, 李成钢. 基于样本均衡与特征交互的通信网络故障预测方法[J]. 北京邮电大学学报, 2021, 44(6): 59-66.
[10]	普运伟, 郭江, 刘涛涛, 吴海潇. 基于多学习单元卷积神经网络的雷达辐射源信号识别[J]. 北京邮电大学学报, 2021, 44(6): 74-82.
[11]	张雷, 陈川, 谭淇匀, 郝茂鑫, 杨学康. 结合S盒与混沌映射的图像加密算法[J]. 北京邮电大学学报, 2021, 44(6): 40-47.
[12]	王艺霏, 莫爽, 吴文睿, 范少华, 肖丁. 基于内外卷积网络的网络入侵检测[J]. 北京邮电大学学报, 2021, 44(5): 94-100.
[13]	高慧, 张继威, 来扬, 王文东. 深度学习的人体图像半自动标注系统[J]. 北京邮电大学学报, 2021, 44(1): 104-109.
[14]	蒲悦逸, 王文涵, 朱强, 陈朋朋. 基于CNN-ResNet-LSTM模型的城市短时交通流量预测算法[J]. 北京邮电大学学报, 2020, 43(5): 9-14.
[15]	张天麒, 康波, 孟祥飞, 刘奕琳, 周颖. 基于U-Net的颅内出血识别算法[J]. 北京邮电大学学报, 2020, 43(3): 92-98.

基于深度学习的类SM4算法S盒逆向分析

Reverse-Analysis of S-Box for SM4-Like Algorithms Based on Side Channel Technology

RichHTML

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

本文评价