密码学智能化研究进展与分析

doi:10.11896/jsjkx.220300053

计算机科学 ›› 2022, Vol. 49 ›› Issue (9): 288-296.doi: 10.11896/jsjkx.220300053

密码学智能化研究进展与分析

宁晗阳¹, 马苗^1,2, 杨波¹, 刘士昌¹

1 陕西师范大学计算机科学学院西安 710119
2 现代教学技术教育部重点实验室(陕西师范大学) 西安 710062

收稿日期:2022-03-07 修回日期:2022-06-08 出版日期:2022-09-15 发布日期:2022-09-09
通讯作者: 马苗([email protected])
作者简介:([email protected])
基金资助:
国家自然科学基金(U2001205,61877038);陕西师范大学研究生创新团队项目课题(TD2020044Y);中央高校基本科研业务费专项资金资助(2021CSLY021,GK202007033).

Research Progress and Analysis on Intelligent Cryptology

NING Han-yang¹, MA Miao^1,2, YANG Bo¹, LIU Shi-chang¹

1 School of Computer Science,Shaanxi Normal University,Xi'an 710119,China
2 Key Laboratory of Modern Teaching Technology of Ministry of Education(Shaanxi Normal University),Xi'an 710062,China

Received:2022-03-07 Revised:2022-06-08 Online:2022-09-15 Published:2022-09-09
About author:NING Han-yang,born in 1996,postgraduate.His main research interests include information security and crowd sensing.
MA Miao,born in 1977,Ph.D,professor,Ph.D supervisor.Her main research interests include information security and application of swarm intelligence.
Supported by:
National Natural Science Foundation of China(U2001205,61877038),Project of Innovation Team for Graduate Students of Shaanxi Normal University(TD2020044Y) and Fundamental Research Funds for the Central Universities(2021CSLY021,GK202007033).

摘要/Abstract

摘要： 人工智能、5G网络技术的迅速发展开启了万物互联的新时代,计算能力的大幅提高使得基于计算困难性理论的传统密码算法受到威胁,数据安全和通讯安全已成为物联网时代亟待解决的首要问题,密码学由此进入智能化时代。新一代智能化密码学包括基于神经网络的智能密码算法和以机器学习为工具的智能密码分析这两大核心技术。前者利用神经网络的非线性特征设计加密过程,提高密文安全性;后者通过明密文数据集训练机器学习模型获得密文特征,提高密文破译效率。文中简要回顾了密码算法的发展历程,论述了密码学智能化常用的机器学习方法,重点梳理了国内外密码算法及密码分析智能化的最新进展,分析了目前密码学智能化的优势与不足,并探讨了未来的研究方向和面临的挑战。

关键词: 机器学习, 人工神经网络, 密码学, 智能密码算法

Abstract: The rapid development of artificial intelligence and 5G network technology has opened a new era of interconnection of all things.The great improvement of computing power has threatened the traditional cryptographic algorithm based on the theory of computational difficulty.Data security and communication security have become key problems to be solved urgently in the era of Internet of things,hence cryptology has entered an intelligence era.The new generation of intelligent cryptology mainly consists of two core technologies:intelligent cryptographic algorithm based on neural network and intelligent cryptanalysis based on machine learning.The former uses the nonlinear characteristics of neural network to design the encryption process and improve the security of ciphertext.The latter trains the machine learning model through the clear ciphertext set to obtain the ciphertext features and improve the ciphertext decoding efficiency.This paper briefly reviews the development of cryptographic algorithms,discusses machine learning methods on intelligent cryptology,focuses on combing the latest progress of cryptographic algorithms and cryptanalysis intelligence at home and abroad,analyzes the advantages and disadvantages of intelligent cryptology at present,and discusses the research direction and challenges in the future.

Key words: Machine learning, Artificial neural networks, Cryptology, Intelligent cryptographic algorithm

中图分类号:

TP309.7

宁晗阳, 马苗, 杨波, 刘士昌. 密码学智能化研究进展与分析[J]. 计算机科学, 2022, 49(9): 288-296. https://doi.org/10.11896/jsjkx.220300053

NING Han-yang, MA Miao, YANG Bo, LIU Shi-chang. Research Progress and Analysis on Intelligent Cryptology[J]. Computer Science, 2022, 49(9): 288-296. https://doi.org/10.11896/jsjkx.220300053

参考文献

[1]XIANG J Z.Using legalization to promote password intelligence to achieve the credibility of active immunization-an exclusive interview with Shen Changxiang,a member of the Chinese Academy of Engineering and a cryptologist[J].China Information Security,2019,119(11):65-68.
[2]SHANNON C E.Communication theory of secrecy systems[J].Bell System Technical Journal,1949,28(4):656-715.
[3]WANG B C,JIA W J,CHEN Y G.Status quo,Application and trend of cryptography[J].Radio Communications Technology,2019,45(1):1-8.
[4]FENG D G.Research on theory and approach of provable secu-rity[J].Journal of Software,2005,16(10):1743-1756.
[5]KERCKHOFFS A.La cryptographie militaire[J].Des Sciences Militaires,1883,IX:5-38.
[6]KOCHER P,JAFFE J,JUN B.Differential power analysis[C]//Proceedings of the Cryptology.1999:789-789.
[7]KNUDSEN L R,ROBSHAW M J B,WAGNER D.Truncated differentials and skipjack[C]//Proceedings of CRYPTO.1999:165-180.
[8]GOODFELLOW I,POUGET-ABADIE J,MIRZA M,et al.Ge-nerative adversarial nets[C]//Proceedings of the 27th Confe-rence on Advances in Neural Information Processing Systems.2014:2672-2680.
[9]RIVEST R L.Cryptography andmachine learning[C]//Procee-dings of Advances in Cryptology.1991:427-439.
[10]JOLLANDA S.Some applications of machine learning in cryptography[C]//Proceedings of ICSNS-VIII.2020:1-9.
[11]ALANI M M.Applications of machine learning in cryptography:a survey[C]//Proceedings of the 3rd International Confe-rence on Cryptography,Security and Privacy.2019:23-27.
[12]PATTANAYAK S,LUDWIG S A.Encryption based on neuralcryptography[C]//Proceedings of the International Conference on Hybrid Intelligent Systems.2017:1-4.
[13]KINZEL W,KANTER I.Neural cryptography[C]//Procee-dings of the 9th International Conference on Neural Information Processing.2002:1351-1354.
[14]ROSEN Z M,KLEIN E,KANTER I,et al.Mutual learning in a tree parity machine and its application to cryptography[J].Physical Review E Statistical Nonlinear & Soft Matter Physics,2002,66(6):66-135.
[15]KLEIN E,MISLOVATY R,KANTER I,et al.Synchronization of neural networks by mutual learning and its application to cryptography[C]//Proceedings of the Neural Information Processing Systems.2005:689-696.
[16]CHAKRABORTY S,DALAL J,SARKAR B,et al.Neural synchronization based secret key exchange over public channels:a survey[C]//Proceedings of the International Conference on Signal Propagation and Computer Technology.2014:368-375.
[17]JAYANTA K P,MANDAL J K.A random block length based cryptosystem through multiple cascaded permutation combinations and chaining of blocks[C]//Proceedings of the Interna-tional Conference on Industrial and Information Systems(ICIIS).2009:26-31.
[18]MANDAL J K,SARKAR A.An adaptive neural network guided secret key based encryption through recursive positional modulo-2 substitution for online wireless communication[C]//Proceedings of the International Conference on Recent Trends in Information Technology.2011:107-112.
[19]MISLOVATY R,PERCHENOK Y,KANTER I,et al.Securekey-exchange protocol with an absence of injective functions[J].Physical Review E,2002,66(6):102-107.
[20]LIANG Y.Design and analysis of neural key-exchange protocol[D].Chongqing:Chongqing University,2014.
[21]LI L,ZHOU S.Research on key agreement algorithm based on neural network synchronization[J].Journal of Chongqing University of Technology(Natural Sciences Edition),2015,29(8):104-110.
[22]ZHANG L,LIU F,DONG T,et al.Neural cryptography algorithm based on “Do not Trust My Partner” and fast learning rule[J].Journal of Computer Applications,2015,35(6):1683-1687.
[23]DOROKHIN E S,FUERTES W,LASCANO E.On the development of an optimal structure of tree parity machine for the establishment of a cryptographic key[J/OL].Security and Communication Networks,2019:1-10.https://www.hindawi.com/journals/scn/2019/8214681/.
[24]TAO D,HUANG T.Neural cryptography based on complex-valued neural network[J].IEEE Transactions on Neural Networks and Learning Systems,2019,31(11):1-6.
[25]SARKAR A,KHAN M Z,SINGH M M,et al.Artificial neural synchronization using nature inspired whale optimization[J].IEEE Access,2021,9:16435-16447.
[26]JEONG S,PARK C,HONG D,et al.Neural cryptography based on generalized tree parity machine for real-life systems[J].Security and Communication Networks,2021,2021(11):1-12.
[27]ABADI M,ANDERSEN D G.Learning to protect communications with adversarial neural cryptography[C]//Proceedings of the International Conference on Learning Representations.2016:1-15.
[28]COUTINHO M,DE OLIVEIRA ALBUQUERQUE R,BORGES F,et al.Learning perfectly secure cryptography to protect communications with adversarial neural cryptography[J].Sensors,2018,18(5):1306.
[29]ZHOU X,WANG C,JING X.Componential design of crypto-graphic algorithm based on generative adversarial method[J].Journal of Beijing Electronic Science and Technology Institute,2020,28(4):1-15.
[30]YAN X,CUI B,XU Y,et al.A method of information protection for collaborative deep learning under GAN model attack[J].IEEE-ACM Transactions on Computational Biology and Bioinformatics,2021,18(3):871-881.
[31]DING Y,WU G,CHEN D,et al.DeepEDN:A deep-learning-based image encryption and decryption network for Internet of medical things[J].IEEE Internet of Things Journal,2021,8(3):1504-1518.
[32]WU J,XIA W,ZHU G,et al.Image encryption based on adversarial neural cryptography and SHA controlled chaos[J].Journal of Modern Optics,2021,68(8):409-418.
[33]ZHANG H,ZHOU S B.Application of chaos theory in cryptography[J].Journal of Chongqing University,2004,27(4):39-43.
[34]SU S,LIN A,YEN J C.Design and realization of a new chaotic neural encryption decryption network[C]//Proceedings of the IEEE Asia-Pacific Conference on Circuits and Systems.Electronic Communication Systems,2000:335-338.
[35]LIU N,DONG H.Security analysis of public-key encryptionscheme based on neural networks and its implementing[C]//Proceedings of the International Conference on Computational Intelligence and Security.2006:1327-1330.
[36]ZOU A,XIU X.An asynchronous encryption arithmetic based on laguerre chaotic neural networks[C]//Proceedings of the WRI Global Congress on Intelligent Systems.2009:36-39.
[37]XIAO C L,SUN Y,LIN B J,et al.Double encryption method based on neural network and composite discrete chaotic system[J].Journal of Electronics & Information Technology,2020,42(3):687-694.
[38]FANG P,LIU H,WU C.A novel chaotic block image encryption algorithm based on deep convolutional generative adversa-rial networks[J].IEEE Access,2021,9:18497-18517.
[39]ARVANDI M,WU S,SADEGHIAN A,et al.Symmetric cipher design using recurrent neural networks[C]//Proceedings of the IEEE International Joint Conference on Neural Network.2006:2039-2046.
[40]ARVANDI M,WU S,SADEGHIAN A.On the use of recurrent neural networks to design symmetric ciphers[J].IEEE Computational Intelligence Magazine,2008,3(2):42-53.
[41]SHI J,CHEN S,LU Y,et al.An approach to cryptographybased on continuous-variable quantum neural network[J].Scientific Reports,2020,10(7):2107-2120.
[42]SAGAR V,KUMAR K.A symmetric key cryptographic algorithm using counter propagation network[C]//Proceedings of the ACM sponsored International Conference on Information and Communication Technology for Competitive Strategies.2014:1-5.
[43]LU X,CHEN Y,LI X.Hierarchical Recurrent Neural Hashing for Image Retrieval with Hierarchical Convolutional Features[J].IEEE Transactions on Image Processing,2018,27(1):106-120.
[44]LU H,ZHANG M,XU X,et al.Deep Fuzzy Hashing Network for Efficient Image Retrieval[J].IEEE Transactions on Fuzzy Systems,2021,29(1):166-176.
[45]BACKES M,DURMUTH M,GERLING S,et al.Acoustic side-channel attacks on printers[C]//Proceedings of the USENIX Security symposium.2010:307-322.
[46]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):293-300.
[47]HEUSER A,ZOHNER M.Intelligent machine homicide[C]//Proceedings of International Workshop on Constructive Side-Channel Analysis and Secure Design.2012:249-264.
[48]BARKEWITZ T,LEMKERUST K.Efficient template attacksbased on probabilistic multi-class support vector machines[C]//Proceedings of International Conference on Smart Card Research and Advanced Applications.2012:263-276.
[49]LERMAN L,BONTEMPI G,MARKOWITHCH O.A machine learning approach against a masked AES[J].Journal of Cryptographic Engineering,2015,5(2):123-139.
[50]PANCHENKO A,NIESSEN L,ZINNEN A,et al.Website finger-printing in onion routing based anonymization networks[C]//Proceedings of the 10th Annual ACM Workshop on Privacy in the Electronic Society.2011:103-114.
[51]CAI X,ZHANG X C,JOSHI B,et al.Touching from a distance:website fingerprinting attacks and defenses[C]//Proceedings of the 2012 ACM Conference on Computer and Communications Security.2012:605-616.
[52]WANG T,GOLDBERG I.Improved website fingerprinting on Tor[C]//Proceedings of the 12th Annual ACM Workshop on Privacy in the Electronic Society.2013:201-212.
[53]WANG T,CAI X,NITHYA NANG R,et al.Effective attacks and provable defenses for website finger-printing[C]//Procee-dings of the 23rd USENIX Security Symposium.USENIX Association,2014:143-157.
[54]HAYES J,DANEZIS G.K-fingerprinting:a robust scalablewebsite fingerprinting technique[C]//Proceedings of the 25rd USENIX Security Symposium.2016:1187-1203.
[55]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.
[56]MARTINASEK Z,HAJNY J,MALINA L.Optimization ofpower analysis using neural network[C]//Proceeding of the International Conference on Smart Card Research and Advanced Applications.2013:94-107.
[57]CAGLI E,DUMAS C,PROUFF E.Convolutional neural networks with data augmentation against jitter-based counter measures[C]//Proceeding of the Cryptographic Hardware and Embedded Systems.2017:45-68.
[58]TIMON B.Non-profiled deep learning-based side-channel at-tacks with sensitivity analysis[J].IACR Transactions on Cryptographic Hardware and Embedded Systems,2019(2):107-131.
[59]RIMMER V,PREUVENEERS D,JUAREZ M,et al.Automated website fingerprinting through deep learning[C]//Procee-dings of the 25th Annual Network and Distributed System Secu-rity Symposium.2018:1-15.
[60]SIRINAM P,IMANI M,JUAREZ M,et al.Deep fingerprinting:undermining website fingerprinting defenses with deep learning[C]//Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security.2018:1928-1943.
[61]BHAT S,LU D,KWON A,et al.Var-CNN:A data-efficientwebsite fingerprinting attack based on deep learning[J].Proceedings on Privacy Enhancing Technologies,2019(4):292-310.
[62]RAHMAN M S,SIRINAM P,MATTHEWS N,et al.Tik-Tok:the utility of packet timing in website fingerprinting attacks[C]//Proceeding of the Privacy Enhancing Technologies.2020:1-20.
[63]ALANI M M.Neuro-Cryptanalysis of DES and Triple-DES[C]//Proceeding of the International Conference on Neural Information Processing.2012:637-646.
[64]JAYACHANDIRAN K.A machine learning approach for cryptanalysis[R/OL].Rocheste:Rochester Institute of Technology,2018.https://www.semanticscholar.org/paper/A-Machine-Lear-ning-Approach-for-Cryptanalysis-Jayachandiran/e1616cdb40415a6444a4b2dbfbf197d60bcc43d3#:~:text=%EE%80%80A%20Machine%20Learning%20Approach%20for%20Cryptanaly-sis%EE%80%81.%20The%20paper,key%20that%20was%20used%20to%20encrypt%20the%20plaintext.
[65]TENG N,LU H,JING M,et al.PG-RNN:a password-guessing model based on recurrent neural networks[J].CAAI Transactions on Intelligent Systems,2018,13(6):889-896.
[66]BOST R,POPA R A,TU S,et al.Machine learning classification over encrypted data[C]//Proceeding of the Network and Distributed System Security Symposium.2014:331-346.
[67]HILL G D,BELLEKENS X J A.Deep learning based crypto-graphic primitive classification[J].arXiv:1709.08385,2017.
[68]GUPTA M,DESHMUKH M.Single secret image sharingscheme using neural cryptography[J].Multimedia Tools and Applications,2020,79(12):183-204.
[69]XIE P,BILENKO M,FINLEY T,et al.Crypto-Nets:neural networks over encrypted data[J].arXiv:1412.6181,2014.
[70]LI X J,WU G W,YAO L,et al.Progress and future challenges of security attacks and defense mechanisms in machine learning[J].Journal of Software,2021,32(2):406-423.
[71]SUN L,LI H,YU S W,et al.A survey on encrypted image re-cognition models[J].Journal of Cryptologic Research,2020,7(4):525-540.
[72]JI S L,DU T Y,LI J F,et al.Security and privacy of machine learning models:a survey[J].Journal of Software,2021,32(1):41-67.
[73]WEI L W,CHEN C,ZHANG L,et al.Security issues and privacy preserving in machine learning[J].Journal of Computer Research and Development,2020,57(10):2066-2085.
[74]HE Y Z,HU X B,HE J W,et al.Privacy and security issues in machine learning systems:a survey[J].Journal of Computer Research and Development,2019,56(10):2049-2070.
[75]ALSHAMMARI R,ZINCIR-HEYWOOD A N.Machine lear-ning based encrypted traffic classification:Identifying SSH and Skype[C]//IEEE Symposium on Computational Intelligence for Security and Defense Applications.2009:1-8.

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密码学智能化研究进展与分析

Research Progress and Analysis on Intelligent Cryptology

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