基于卷积神经网络的旁路密码分析综述

doi:10.11896/jsjkx.210300286

摘要/Abstract

摘要： 旁路建模分析方法可以有效攻击密码实现,其中基于卷积神经网络的旁路密码分析方法(CNNSCA)可以高效地进行密码攻击,甚至能够攻击有防护的加密算法设备。针对现阶段旁路密码分析建模方法的研究现状,对比分析了几种CNNSCA的模型特点和性能差异,并针对典型CNN模型结构以及旁路信号公共数据集ASCAD,通过模型对比及实验结果分析不同的CNN网络建模方法的效果,进而分析影响CNNSCA方法的性能因素、基于卷积神经网络的旁路建模方法的优势。由分析可知,基于VGG变体的CNNSCA在攻击各种情况的目标数据集时泛化性、鲁棒性表现最好,但使用的CNN模型训练程度及超参数设置是否最适用于SCA场景并未得到验证。今后研究者可通过调整CNN模型的各种超参数,使用数据增强技术,结合Imagenet大赛中优秀CNN网络等手段,来提升CNNSCA的分类准确率和破密性能,探索最适用于SCA场景的CNN模型是未来的发展趋势。

关键词: 超参数, 建模方法, 卷积神经网络, 旁路分析, 性能评估

Abstract: The profiled side-channel analysis method can effectively attack the implementation of cryptographic,and the side-channel cryptanalysis method based on convolutional neural network (CNNSCA) can efficiently carry out cryptographic attacks,and even can attack the implementation of protected encryption algorithms.In view of the current research status of side-channel cryptanalysis profiling methods,this paper compares and analyzes the characteristics and performance differences of several CNNSCA models,and focuses on the typical CNN model structure and side-channel signal public data set ASCAD.Through model comparison and experimental results,it compares and analyzes the effects of different CNN network modeling methods,and then analyzes the performance factors that affect the CNNSCA method and the advantages of the side-channel profiling method based on convolutional neural networks.Research and analysis show that CNNSCA based on VGG variants performs best in generalization and robustness when attacking target data sets in various situations,but whether the training level of the used CNN model and the hyperparameter settings are most suitable for SCA scenarios have not been verified.In the future,researchers can improve the classification accuracy and decryption performance of CNNSCA by adjusting various hyperparameters of the CNN model,use data enhancement techniques and combine the excellent CNN network in the Imagenet competition to explore the most suitable CNN model for SCA scenarios,which is a development trend.

Key words: Convolutional neural network, Hyperparameter, Performance evaluation, Profiling method, Side-channel analysis

中图分类号:

TP309.7

刘林云, 陈开颜, 李雄伟, 张阳, 谢方方. 基于卷积神经网络的旁路密码分析综述[J]. 计算机科学, 2022, 49(5): 296-302. https://doi.org/10.11896/jsjkx.210300286

LIU Lin-yun, CHEN Kai-yan, LI Xiong-wei, ZHANG Yang, XIE Fang-fang. Overview of Side Channel Analysis Based on Convolutional Neural Network[J]. Computer Science, 2022, 49(5): 296-302. https://doi.org/10.11896/jsjkx.210300286

参考文献

[1]MANGARD S,OSWALD E,POPP T.Energy analysis attack[M].Beijing:Science Press,2010.
[2]KOCHER P,JAFFE J,JUN B.Differential power analysis[C]//Annual International Cryptology Conference.Berlin:Springer,1999:388-397.
[3]BRIER E,CLAVIER C,OLIVIER F.Correlation power analysis with a leakage model[C]//International Workshop on Cryptographic Hardware and Embedded Systems.Berlin:Springer,2004:16-29.
[4]GIERLICHS B,BATINA L,TUYLS P,et al.Mutual information analysis[C]//International Workshop on Cryptographic Hardware and Embedded Systems.Berlin:Springer,2008:426-442.
[5]CHARI S,RAO J R,ROHATGI P.Template attacks[C]//International Workshop on Cryptographic Hardware and Embedded Systems.Berlin:Springer,2002:13-28.
[6]LERMAN L,BONTEMPI G,MARKOWITCH O.Power analysis attack:An approachbased on machine learning[J].International Journal of Applied Cryptography:IJACT,2014,3(2):97-115.
[7]PICEK S,HEUSER A,GUILLEY S.Template attack versus Ba-yes classifier[J].Journal of Cryptographic Engineering,2017,7(4):1-9.
[8]CAGLI E,DUMAS C,PROUFF E.Convolutional Neural Net-works with Data Augmentation Against Jitter-Based Countermeasures Profiling Attacks Without Preprocessing[C]//Cryptographic Hardware and Embedded Systems CHES 2017 19th International Conference.Taipei,Taiwan,2017:45-68.
[9]CHOUDARY O,KUHN M G.Efficient template attacks[C]//International Conference on Smart Card Research and Advanced Applications.Springer,2013:253-270.
[10]LERMAN L,POUSSIER R,BONTEMPI G,et al.Template Attacks vs.Machine Learning Revisited (and the Curse of Dimensionality in Side-Channel Analysis)[C]//Constructive Side-Channel Analysis and Secure Design-6th International Workshop,COSADE 2015.Berlin,Germany,2015:20-33.
[11]LERMAN L,BONTEMPI G,MARKOWITCH O.A machinelearning approach against a masked AES-Reaching the limit of side-channel attacks with a learning model[J].Jounal of Cryptographic Engineering,2015,5(2):123-139.
[12]PICEK S,HEUSER A,JOVIC A,et al.Climbing down the hierarchy:Hierarchical classification for machine learning side-channel attacks[C]//9th International Conference on Cryptology in Africa.Springer,2017:61-78.
[13]HEUSER A,ZOHNER M.Intelligent Machine Homicide Brea-king Cryptographic Devices Using Support Vector Machines[C]//COSADE.Springer,2012:249-264.
[14]HOSPODAR G,GIERLICHS B,DE MULDER E,et al.Ma-chine learning in side-channel analysis:a first study[J].Journal of Cryptographic Engineering,2011,1(4):293-302.
[15]PICEK S,HEUSER A,JOVIC A,et al.Side-channel analysis and machine learning:A practical perspective[C]//2017 International Joint Conference on Neural Networks,IJCNN 2017.Anchorage,AK,USA,2017:4095-4102.
[16]MAGHREBI H,PORTIGLIATTI T,PROUFF E.Breaking cryp-tographic implementations using deep learning techniques[C]//6th International Conference on Security,Privacy,and Applied Cryptography Engineering(SPACE 2016).Hyderabad,India,2016:3-26.
[17]BENGIO Y,GOODFELLOW I,COURVILLE A.Deep learning[M].MIT press,2017:170-200.
[18]PICEK S,SAMIOTIS,I P,HEUSER A,et al.On the performance of convolutional neural networks for side-channel analysis[OL].https://eprint.iacr.org/2018/004.
[19]BENADJILA R,PROUFF E,STRULLU R,et al.Deep learning for side-channel analysis and introduction to ASCAD database[J].Journal of Cryptographic Engineering,2019,10.
[20]HEUSER A,PICEK S,GUILLEY S,et al.Lightweight ciphers and their side-channel resilience[J].IEEE Transactions on Computers,2017,69(10):1434-1448.
[21]HUANG J,WANG Y.Experimental Research on Convolutional Neural Network Structure Suitable for Side Channel Analysis[J].Journal of Chengdu University of Information Technology,2019(5):449-456.
[22]MAGHREBI H.Deep learning based side channel attacks inpractice[J/OL].IACR Cryptol.ePrint Arch.,2019:578.https://eprint.iacr.org/2019/578.
[23]HUANG G,LIU Z,VAN DER MAATEN L,et al.Densely connected convolutional networks[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2017:4700-4708.
[24]HUBEL D H,WIESEL T N.Receptive Fields And Functional Aechitecture of Monkey Striate Cortex[J].The Journal of Phy-siology,1968,195(1):215-243.
[25]LECUN Y,BENGIO Y.Convolutional networks for images,speech,and time series[M]//The Handbook of Brain Theory and Neural Networks.MIT Press,1998:255-258.
[26]SHI H,YANG Q,LIU S H,et al.Research on information extraction of power grid failure plans based on deep learning[J].Computer Science,2020,47(S2):62-66.
[27]YIN W,KANN K,YU M,et al.Comparative Study of CNN and RNN for Natural Language Processing[J].arXIV:1702.01923,2017.
[28]RUSSAKOVSKY O,DENG J,SU H,et al.Imagenet largescale visual recognition challenge[J].International Journal of Computer Vision,2015,115(3):211-252.
[29]GILMORE R,HANLEY N,O’NEILL M.Neural networkbased attack on a masked implementation of AES[C]//2015 IEEE International Symposium on Hardware Oriented Security and Trust (HOST).2015:106-111.
[30]ZOTKIN Y,OLIVIER F,BOURBAO E.Deep Learning vsTemplate Attacks in front of fundamental targets:experimental study[J/OL].IACR.https://xs.dailyheadlines.cc/scholar?q=Deep+Learning+vs+Template+Attacks+in+front+of+fundamental+targets%3A+experimental+study
[31]IOFFE S,SZEGEDY C.Batch normalization:accelerating deep network training by reducing internal covariate shift[J].arXiv:1502.03167,2015.
[32]GOODFELLOW I J,BENGIO Y,COURVILLE A C.DeepLearning[M]//Adaptive Computation and Machine Learning.Cambridge:MIT Press,2016.
[33]HAN L Q,KANG Q.Artificial Neural Network Theory,Design and Application——Nerve Cells,Neural Networks and Neural System[J].Journal of Beijing Technology and Business University(Natural Science Edition),2005,23(1):52-52.
[34]HAWKINS D M.The problem of overfitting[J].Journal ofChemical Information and Computer Sciences,2004,44(1):1-12.
[35]STANDAERT F X,MALKIN T G,YUNG M.A unified framework for the analysis of side-channel key recovery attacks[C]//Annual International Conference on The Theory and Applications of Cryptographic Techniques.Berlin:Springer,2009:443-461.
[36]MASURE L,DUMAS C,PROUFF E.A comprehensive study of deep learning for side-channel analysis[J].IACR Transactions on Cryptographic Hardware and Embedded Systems,2020(1):348-375.
[37]OORD A V D,DIELEMAN S,ZEN H,et al.Wavenet:A generative model for raw audio[J].arXiv:1609.03499,2016.
[38]CAGLI E,DUMAS C,PROUFF E.Convolutional neural networks with data augmentation against jitter-based countermeasures[C]//International Conference on Cryptographic Hardware and Embedded Systems.Cham:Springer,2017:45-68.
[39]WONG S C,GATT A,STAMATESCU V,et al.Understanding data augmentation for classification:when to warp?[C]//International Conference on Digital Image Computing:Techniques and Applications (DICTA).IEEE,2016:1-6.
[40]SIMONYAN K,ZISSERMAN A.Very deep convolutional networks for large scale image recognition[J].arXiv:1409.1556,2014.
[41]SZEGEDY C,LIU W,JIA Y,et al.Going deeper with convolutions[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2015:1-9.
[42]HE K,ZHANG X,REN S,et al.Deep residual learning for image recognition[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2016:770-778.
[43]MASURE L,DUMAS C,PROUFF E.Gradient visualization for general characterization in profiling attacks[C]//International Workshop on Constructive Side-Channel Analysis and Secure Design.Cham:Springer,2019:145-167.
[44]CARBONE M,CONIN V,CORNÉLIE M A,et al.Deep learning to evaluate secure RSA implementations[J].IACR Transactions on Cryptographic Hardware and Embedded Systems,2019(2):132-161.
[45]KIM J,PICEK S,HEUSER A,et al.Make some noise.unleashing the power of convolutional neural networks for profiled side-channel analysis[J].IACR Transactions on Cryptographic Hardware and Embedded Systems,2019(3):148-179.
[46]CHEN P,WANG P,DONG G F,et al.Side channel attackbased on SincNet[J/OL].Journal of Cryptography.http://kns.cnki.net/kcms/detail /10.1195.TN.20200520.1652.002.html.
[47]PERIN G,BUHAN I,PICEK S.Learning when to stop:a mutual information approach to fight overfitting in profiled side-channel analysis [C]//International Workshop on Constructive Side-Channel Analysis and Secure Design.Springer,Cham,2021:53-81.
[48]GUO D X,CHEN K Y,ZHANG Y,et al.A new method for attacking encrypted chip templates based on Alexnet convolutional neural network[J].Computer Measurement and Control,2018,26(10):246-249,254.
[49]GUO D X,CHEN K Y,ZHANG Y,et al.A new method of attacking encrypted chip templates based on VGGNet convolutional neural network[J].Computer Application Research,2019,36(9):2809-2812,2855.
[50]GULLI A,PAL S.Deep learning with Keras[M].Packt Publishing Ltd,2017.
[51]ABADI M,AGARWAL A,BARHAM P,et al.Tensor Flow:Large-scale machine learning on heterogeneous systems[OL].https://www.tensorflow.org/.Software available from tensorflow.org.

相关文章 15

[1]	周乐员, 张剑华, 袁甜甜, 陈胜勇. 多层注意力机制融合的序列到序列中国连续手语识别和翻译 Sequence-to-Sequence Chinese Continuous Sign Language Recognition and Translation with Multi- layer Attention Mechanism Fusion 计算机科学, 2022, 49(9): 155-161. https://doi.org/10.11896/jsjkx.210800026
[2]	李宗民, 张玉鹏, 刘玉杰, 李华. 基于可变形图卷积的点云表征学习 Deformable Graph Convolutional Networks Based Point Cloud Representation Learning 计算机科学, 2022, 49(8): 273-278. https://doi.org/10.11896/jsjkx.210900023
[3]	陈泳全, 姜瑛. 基于卷积神经网络的APP用户行为分析方法 Analysis Method of APP User Behavior Based on Convolutional Neural Network 计算机科学, 2022, 49(8): 78-85. https://doi.org/10.11896/jsjkx.210700121
[4]	朱承璋, 黄嘉儿, 肖亚龙, 王晗, 邹北骥. 基于注意力机制的医学影像深度哈希检索算法 Deep Hash Retrieval Algorithm for Medical Images Based on Attention Mechanism 计算机科学, 2022, 49(8): 113-119. https://doi.org/10.11896/jsjkx.210700153
[5]	檀莹莹, 王俊丽, 张超波. 基于图卷积神经网络的文本分类方法研究综述 Review of Text Classification Methods Based on Graph Convolutional Network 计算机科学, 2022, 49(8): 205-216. https://doi.org/10.11896/jsjkx.210800064
[6]	金方焱, 王秀利. 融合RACNN和BiLSTM的金融领域事件隐式因果关系抽取 Implicit Causality Extraction of Financial Events Integrating RACNN and BiLSTM 计算机科学, 2022, 49(7): 179-186. https://doi.org/10.11896/jsjkx.210500190
[7]	张颖涛, 张杰, 张睿, 张文强. 全局信息引导的真实图像风格迁移 Photorealistic Style Transfer Guided by Global Information 计算机科学, 2022, 49(7): 100-105. https://doi.org/10.11896/jsjkx.210600036
[8]	戴朝霞, 李锦欣, 张向东, 徐旭, 梅林, 张亮. 基于DNGAN的磁共振图像超分辨率重建算法 Super-resolution Reconstruction of MRI Based on DNGAN 计算机科学, 2022, 49(7): 113-119. https://doi.org/10.11896/jsjkx.210600105
[9]	刘月红, 牛少华, 神显豪. 基于卷积神经网络的虚拟现实视频帧内预测编码 Virtual Reality Video Intraframe Prediction Coding Based on Convolutional Neural Network 计算机科学, 2022, 49(7): 127-131. https://doi.org/10.11896/jsjkx.211100179
[10]	徐鸣珂, 张帆. Head Fusion:一种提高语音情绪识别的准确性和鲁棒性的方法 Head Fusion:A Method to Improve Accuracy and Robustness of Speech Emotion Recognition 计算机科学, 2022, 49(7): 132-141. https://doi.org/10.11896/jsjkx.210100085
[11]	孙福权, 崔志清, 邹彭, 张琨. 基于多尺度特征的脑肿瘤分割算法 Brain Tumor Segmentation Algorithm Based on Multi-scale Features 计算机科学, 2022, 49(6A): 12-16. https://doi.org/10.11896/jsjkx.210700217
[12]	李亚茹, 张宇来, 王佳晨. 面向超参数估计的贝叶斯优化方法综述 Survey on Bayesian Optimization Methods for Hyper-parameter Tuning 计算机科学, 2022, 49(6A): 86-92. https://doi.org/10.11896/jsjkx.210300208
[13]	吴子斌, 闫巧. 基于动量的映射式梯度下降算法 Projected Gradient Descent Algorithm with Momentum 计算机科学, 2022, 49(6A): 178-183. https://doi.org/10.11896/jsjkx.210500039
[14]	杨涵, 万游, 蔡洁萱, 方铭宇, 吴卓超, 金扬, 钱伟行. 基于步态分类辅助的虚拟IMU的行人导航方法 Pedestrian Navigation Method Based on Virtual Inertial Measurement Unit Assisted by GaitClassification 计算机科学, 2022, 49(6A): 759-763. https://doi.org/10.11896/jsjkx.211200148
[15]	杨玥, 冯涛, 梁虹, 杨扬. 融合交叉注意力机制的图像任意风格迁移 Image Arbitrary Style Transfer via Criss-cross Attention 计算机科学, 2022, 49(6A): 345-352. https://doi.org/10.11896/jsjkx.210700236

Metrics

Viewed

Full text

Abstract

Cited

Shared

Discussed