基于卷积神经网络的旁路密码分析综述

doi:10.11896/jsjkx.210300286

Abstract

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

CLC Number:

TP309.7

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.

References

[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.

Metrics

Viewed

Full text

Abstract

Cited

Shared

Discussed

Comments

Recommended 0

No Suggested Reading articles found!

Overview of Side Channel Analysis Based on Convolutional Neural Network

PDF (PC)

Abstract

Cite this article

share this article

References

Related Articles 15

Metrics

Comments

Recommended 0

[1]	ZHOU Le-yuan, ZHANG Jian-hua, YUAN Tian-tian, CHEN Sheng-yong. Sequence-to-Sequence Chinese Continuous Sign Language Recognition and Translation with Multi- layer Attention Mechanism Fusion [J]. Computer Science, 2022, 49(9): 155-161.
[2]	CHEN Yong-quan, JIANG Ying. Analysis Method of APP User Behavior Based on Convolutional Neural Network [J]. Computer Science, 2022, 49(8): 78-85.
[3]	ZHU Cheng-zhang, HUANG Jia-er, XIAO Ya-long, WANG Han, ZOU Bei-ji. Deep Hash Retrieval Algorithm for Medical Images Based on Attention Mechanism [J]. Computer Science, 2022, 49(8): 113-119.
[4]	DAI Zhao-xia, LI Jin-xin, ZHANG Xiang-dong, XU Xu, MEI Lin, ZHANG Liang. Super-resolution Reconstruction of MRI Based on DNGAN [J]. Computer Science, 2022, 49(7): 113-119.
[5]	LIU Yue-hong, NIU Shao-hua, SHEN Xian-hao. Virtual Reality Video Intraframe Prediction Coding Based on Convolutional Neural Network [J]. Computer Science, 2022, 49(7): 127-131.
[6]	XU Ming-ke, ZHANG Fan. Head Fusion:A Method to Improve Accuracy and Robustness of Speech Emotion Recognition [J]. Computer Science, 2022, 49(7): 132-141.
[7]	WU Zi-bin, YAN Qiao. Projected Gradient Descent Algorithm with Momentum [J]. Computer Science, 2022, 49(6A): 178-183.
[8]	YANG Yue, FENG Tao, LIANG Hong, YANG Yang. Image Arbitrary Style Transfer via Criss-cross Attention [J]. Computer Science, 2022, 49(6A): 345-352.
[9]	YANG Jian-nan, ZHANG Fan. Classification Method for Small Crops Combining Dual Attention Mechanisms and Hierarchical Network Structure [J]. Computer Science, 2022, 49(6A): 353-357.
[10]	ZHANG Jia-hao, LIU Feng, QI Jia-yin. Lightweight Micro-expression Recognition Architecture Based on Bottleneck Transformer [J]. Computer Science, 2022, 49(6A): 370-377.
[11]	WANG Jian-ming, CHEN Xiang-yu, YANG Zi-zhong, SHI Chen-yang, ZHANG Yu-hang, QIAN Zheng-kun. Influence of Different Data Augmentation Methods on Model Recognition Accuracy [J]. Computer Science, 2022, 49(6A): 418-423.
[12]	SUN Jie-qi, LI Ya-feng, ZHANG Wen-bo, LIU Peng-hui. Dual-field Feature Fusion Deep Convolutional Neural Network Based on Discrete Wavelet Transformation [J]. Computer Science, 2022, 49(6A): 434-440.
[13]	ZHAO Zheng-peng, LI Jun-gang, PU Yuan-yuan. Low-light Image Enhancement Based on Retinex Theory by Convolutional Neural Network [J]. Computer Science, 2022, 49(6): 199-209.
[14]	ZHANG Wen-xuan, WU Qin. Fine-grained Image Classification Based on Multi-branch Attention-augmentation [J]. Computer Science, 2022, 49(5): 105-112.
[15]	ZHAO Ren-xing, XU Pin-jie, LIU Yao. ECG-based Atrial Fibrillation Detection Based on Deep Convolutional Residual Neural Network [J]. Computer Science, 2022, 49(5): 186-193.