Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Published in:
Cover of the book

2018 | OriginalPaper | Chapter

The Research of Cryptosystem Recognition Based on Randomness Test’s Return Value

Authors : Zhicheng Zhao, Yaqun Zhao, Fengmei Liu

Published in: Cloud Computing and Security

Publisher: Springer International Publishing

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

Feature extraction of ciphertext is a key procedure in cryptosystem recognition task. Varieties of ciphertext’s features are proposed in exited literatures, while feature based on randomness test has derived little attention. In this paper, by segmenting ciphertexts and changing parameter of randomness test, we propose 54 features of ciphertext based on NIST’s 15 randomness tests. As a measure of these features, we choose support vector machine as classifier algorithm to verify its performance in cryptosystem recognition. In experimental settings, we consider 15 situations of 6 cryptosystems’ one to one recognition. The experimental results demonstrate that the application of randomness test in cryptosystem recognition is feasible and necessary. Most of proposed features reach better recognition accuracies than random recognition, which indicates that randomness tests are applicable for cryptosystem recognition applications. And we also conduct further analysis: (a) analyze some features’ recognition performance, and find the relation of some feature’s recognition accuracies and cryptosystem. (b) compared with existed features, part of new features maintain high recognition accuracy with lower dimension and smaller data storage space.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
next chapter Threshold Proxy Re-encryption and Its Application in Blockchain
Literature
1.
2.
Schneier, B.: Applied Cryptography: Protocols, Algorithms, and Source Code in C, 2nd edn. Wiley, New York (1996)MATH
3.
Manjula, R., Anitha, R.: Identification of encryption algorithm using decision tree. Adv. Comput. 133, 237–246 (2011)CrossRef
4.
Maheshwari, P.: Classification of Ciphers. Indian Institute of Technology, Kanpur (2001)
5.
Chou, J.W., Lin, S.D., Cheng, C.M.: On the effectiveness of using state-of-the-art machine learning techniques to launch cryptographic distinguishing attacks. In: Proceedings of the 5th ACM Workshop on Security and Artificial Intelligence, pp. 105–110. ACM, New York (2012)
6.
Wu, Y., Wang, T., Xing, M.: Block ciphers identification scheme based on the distribution character of randomness test values of ciphertext. J. Commun. 36(4), 146–155 (2015)
7.
Dileep, A.D., Sekhar, C.C.: Identification of block ciphers using support vector machines. In: Proceedings of the International Joint Conference on Neural Networks (IJCNN 2006), pp. 2696–2701. IEEE, Gulf Islands (2006)
8.
Rukhin, A., Soto, J., Nechvatal, J.: A statistical test suite for random and pseudorandom number generators for cryptographic applications. Appl. Phys. Lett. 22(7), 1645–1798 (2015)
9.
10.
Gustafson, H., Dawson, E., Nielsen, L.: A computer package for measuring the strength of encryption algorithms. Comput. Secur. 13(8), 687–697 (1994)CrossRef
11.
12.
Lozach, F., Ben-Romdhane, M., Graba, T.: FPGA design of an open-loop true random number generator. In: Proceedings of 2013 Euromicro Conference on Digital System Design (DSD), pp. 615–622. IEEE, Los Alamitos (2013)
13.
Alabaichi, A., Mahmod, R., Ahmad, F.: Randomness analysis of 128 bits blowfish block cipher on ECB and CBC modes. Int. J. Digital Content Technol. Appl. 7(15), 77 (2013)
14.
Souza, W.A., Tomlinson, A.: A distinguishing attack with a neural network. In: Proceedings of the IEEE 13th International Conference on Data Mining Workshops (ICDMW 2013), Dallas, pp. 154–161 (2013)
15.
Nagireddy, S.: A Pattern Recognition Approach to Block Cipher Identification. Indian Institute of Technology, Madras (2008)
16.
Sharif, S.O., Mansoor, S.P.: Performance evaluation of classifiers used for identification of encryption algorithms. ACEEE Int. J. Netw. Secur. 2(04), 42–45 (2011)
17.
Sharif, S.O., Kuncheva, L.I., Mansoor, S.P.: Classifying encryption algorithms using pattern recognition techniques. In: Proceedings of the IEEE International Conference on Information Theory and Information Security (ICITIS), Beijing, China, pp. 1168–1172 (2010)
18.
Huang, L.T., Zhao, Z.C., Zhao, Y.Q.: A two-stage cryptosystem recognition scheme based on random forest. Chin. J. Comput. 41(2), 382–399 (2018)
19.
20.
Rao, M.B.: Classiflcation of RSA and IDEA Ciphers. Indian Institute of Technology, Kanpur (2003)
21.
Griffin, G., Holub, A., Perona, P.: Caltech-256 Object Category Dataset. California Institute of Technology, Pasadedna (2007)
22.
Wu, W., Feng, D., Zhang, W.: Design and Analysis of Block Cipher, 2nd edn. Tsinghua University Press, Beijing (2009)
23.
Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273–297 (1995)MATH
24.
Benhur, A.: Support vector clustering. J. Mach. Learn. Res. 2(2), 125–137 (2002)
25.
Flach, P.: Machine Learning: The Art and Science of Algorithms that Make Sense of Data. Cambridge University Press, New York (2012)CrossRef
26.
Shi, G.D., Kang, F., Gu, H.W.: Research and implementation of randomness tests. Comput. Eng. 35(20), 145–146 (2009)
27.
Chen, H., Feng, D.G., Fan, L.M.: A new statistical test on block ciphers. Chin. J. Comput. 32(4), 595–601 (2009)CrossRef
Metadata
Title
The Research of Cryptosystem Recognition Based on Randomness Test’s Return Value
Authors
Zhicheng Zhao
Yaqun Zhao
Fengmei Liu
Copyright Year
2018
Book
Cloud Computing and Security

Print ISBN: 978-3-030-00014-1

Electronic ISBN: 978-3-030-00015-8

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-030-00015-8_1

Premium Partner

    Image Credits