基于条件生成对抗网络的侧信道攻击技术研究

doi:10.3778/j.issn.1002-8331.2009-0397

摘要/Abstract

摘要： 近年来，深度学习技术广泛应用于侧信道攻击（side channel attack，SCA）领域。针对在基于深度学习的侧信道攻击中训练集数量不足的问题，提出了一种用于侧信道攻击的功耗轨迹扩充技术，使用条件生成对抗网络（conditional generate against network，CGAN）实现对原始功耗轨迹的扩充，并使用深度神经网络进行侧信道攻击。通过选择密码运算中间值的汉明重量（hamming weight，HW）作为CGAN的约束条件，将CGAN生成模拟功耗轨迹作为多层感知器（multi-layer perceptron，MLP）神经网络的训练数据，构建模型实现密钥恢复。通过实验对不同类型训练集的攻击效果进行比较，结果表明，使用CGAN生成的功耗轨迹和原始功耗轨迹具有相同的特征，使用扩充后的功耗轨迹对MLP神经网络进行训练和测试，训练精度和测试精度分别提高15.3%和14.4%。

关键词: 侧信道攻击, 深度学习, 条件生成对抗网络, 多层感知器

Abstract: Deep learning technology has been widely used in the field of side channel attack（SCA） in recent years. To solve the problem of insufficient training set in deep learning side channel attacks, an energy trace expansion technology used in SCA is proposed, conditional generate against network（CGAN） is applied to augment the original energy traces as well as training deep neural networks for side-channel attacks. The hamming weight（HW） of the intermediate value of the cryptographic operation is chosen as the label of CGAN. The simulated energy traces generated by CGAN are used as the training data of the multi-layer perceptron（MLP） neural network, and the model is trained to achieve key recovery. The attack effects of different types of training sets are compared through experiments, it shows that the energy traces generated by CGAN have the same characteristics as the original energy traces. The training accuracy and test accuracy are increased by 15.3% and 14.4% respectively with the usage of augmented traces in the training and test of MLP neural network.

Key words: side channel attack（SCA）, deep learning, conditional generative adversarial network（CGAN）, multilayer perceptron（MLP）

汪晶, 王恺, 严迎建. 基于条件生成对抗网络的侧信道攻击技术研究[J]. 计算机工程与应用, 2022, 58(6): 110-117.

WANG Jing, WANG Kai, YAN Yingjian. Research on Side Channel Attack Technology Based on Conditional Generation Against Network[J]. Computer Engineering and Applications, 2022, 58(6): 110-117.

参考文献

[1] KOCHER P，JAFFE J，JUN B.Differential power analysis[C]//Annual International Cryptology Conference.Berlin，Heidelberg：Springer，1999：388-397.
[2] 王安，葛婧，商宁，等.侧信道分析实用案例概述[J].密码学报，2018，5（4）：383-398.
WANG A，GE J，SHANG N，et al.Practical cases of side-channel analysis[J].Journal of Cryptologic Research，2018，5（4）：383-398.
[3] KOCHER P C.Timing attacks on implementations of Diffie-Hellman，RSA，DSS，and other systems[C]//Proceedings of the 16th Annual International Cryptology Conference on Advances in Cryptology.Berlin Heidelberg：Springer，1996.
[4] BRIER E，CLAVIER C，OLIVIER F.Correlation power analysis with a leakage model[C]//International Workshop on Cryptographic Hardware and Embedded Systems.Berlin，Heidelberg：Springer，2004.
[5] CHARI S，RAO J R，ROHATGI P.Template attacks[C]//International Workshop on Cryptographic Hardware and Embedded Systems.Berlin，Heidelberg：Springer，2002.
[6] HOSPODAR G，GIERLICHS B，DE M E，et al.Machine learning in side-channel analysis：a first study[J].Journal of Cryptographic Engineering，2011，1（4）：293.
[7] MAGHREBI H，PORTIGLIATTI T，PROUFF E.Breaking cryptographic implementations using deep learning techniques[C]//Carlet C，Hasan M A，Saraswat V.International Conference on Security，Privacy and Applied Cryptography Engineering，Hyderabad，India，December 14-18，2016.
[8] CAGLI E，DUMAS C，PROUFF E.Convolutional neural networks with data augmentation against jitter-based countermeasures[C]//Fischer W，Homma N.International Conference on Cryptographic Hardware and Embedded Systems-CHES 2017：19th International Conference，Taipei，Taiwan，China，September 25-28，2017.
[9] BENADJILA R，PROUFF E，ISTRULLU R，et al.Deep learning for side-channel analysis and introduction to ASCAD database[J].Journal of Cryptographic Engineering，2020，10（2）：163-188.
[10] ZAID G，BOSSUET L，HABRARD A，et al.Methodology for efficient CNN architectures in profiling attacks[J].IACR Transactions on Cryptographic Hardware and Embedded Systems，2020（1）：1-36.
[11] LENNERT W，VICTOR A，BENEDIKT G，et al.Revisiting a methodology for efficient CNN architectures in profiling attacks[J].IACR Transactions on Cryptographic Hardware and Embedded Systems，2020（3）：147-168.
[12] 葛景全，屠晨阳，高能.侧信道分析技术概览与实例[J].信息安全研究，2019，5（1）：77-89.
GE J Q，TU C Y，GAO N.Technology overview of side channel analysis[J].Journal of Information Security Research，2019，5（1）：77-89.
[13] WANG P，CHEN P，LUO Z M.Enhancing the performance of practical profiling side-channel attacks using conditional generative adversarial networks[EB/OL].（2020-06）[2020-08-30].https：//eprint.iacr.org/2020/867，2020-7-10.
[14] 王恺，严迎建，郭朋飞，等.基于改进残差网络和数据增强技术的能量分析攻击研究[J].密码学报，2020，7（4）：551-564.
WANG K，YAN Y J，GUO P F，et al.Research on power analysis attack based on improved residual network and data augmentation technology[J].Journal of Cryptologic Research，2020，7（4）：551-564.
[15] 王燚，吴震，蔺冰.对加掩加密算法的盲掩码模板攻击[J].通信学报，2019，40（1）：1-14.
WANG Y，WU Z，LIN B.Blind mask template attacks on masked cryptographic algorithm[J].Journal on Communications，2019，40（1）：1-14.
[16] GOODFELLOW I J，POUGET-ABADIE J，MIRZA M，et al.Generative adversarial networks[J].arXiv：1406.2661，
2014.
[17] MIRZA M，OSINDERO S.Conditional generative adversarial nets[J].arXiv：1411.1784，2014.