Strong Pseudorandom Bit Sequence Generators Using Neural Network Techniques and Their Evaluation for Secure Communications | SpringerLink
Skip to main content
Springer Nature Link
Log in

Strong Pseudorandom Bit Sequence Generators Using Neural Network Techniques and Their Evaluation for Secure Communications

  • Conference paper
  • First Online:
AI 2002: Advances in Artificial Intelligence (AI 2002)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2557))

Included in the following conference series:

Abstract

Random components play an especially important role in secure electronic commerce and multimedia communications. For this reason, the existence of strong pseudo random number generators is highly required. This paper presents novel techniques, which rely on artificial neural network architectures, to strengthen traditional generators such as ANSI X.9 based on DES and IDEA. Additionally, this paper proposes a test method for evaluating the required non-predictability property, which also relies on neural networks. This non-predictability test method along with commonly used statistical and nonlinearity tests are suggested as methodology for the evaluation of strong pseudo random number generators. By means of this methodology, traditional and proposed generators are evaluated. The results show that the proposed generators behave significantly better than the traditional, in particular, in terms of nonpredictability.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. D. Gollmann, T. Beth, F. Damm, “Authentication Services in Distributed Systems”, J. Computers & Security, 12 (1993), pp. 753–745.

    Article  Google Scholar 

  2. ITU-T X.509 Authentication Framework

    Google Scholar 

  3. DES77,Data Encryption Standard, Federal Information Processing Standards Publication 46, NBS, January 1977.

    Google Scholar 

    Google Scholar 

  4. Schneier B.,“Applied Cryptography”, J. Willey & Sons, second edition, 1996

    Google Scholar 

  5. ISO 8731-2, “Approved Algorithms for Message Authentication, Part 2: Message Authenticator Algorithm (MAA)”.

    Google Scholar 

  6. W. Diffie, M.E. Hellman: “New Directions in Cryptography”, IEEE Transactions on Information Theory, Vol. 22, No 6, 1976, pp. 644–654.

    Article  MATH  MathSciNet  Google Scholar 

  7. K. Zeng, C-H Yang, D.-Y. Wei, and T.R.N Rao, “Pseudo random Bit Generators in Stream Cipher Cryptography”, IEEE Computer, 8–17, 1991.

    Google Scholar 

  8. A. Shamir, “On the Generation of Cryptographically Strong Pseudorandom Sequences”, J. ACM Transactions on Computer Systems, Vol. 1, No. 1, February 1983, pp. 38–44.

    Article  MathSciNet  Google Scholar 

  9. C. P. Schnorr, “On the Construction of Random Number Generators and Random Function Generators”, Proc. Advances in Cryptology-EUROCRYPT’ 88, Springer-Verlag, 1988, pp. 225–232.

    Google Scholar 

  10. Patterson D. W., “Artificial Neural Networks. Theory and Applications”, Prentice Hall, 1996.

    Google Scholar 

  11. A. M. Law, W. D. Kelton. Simulation Modeling and Analysis, MacGraw-Hill, 1991.

    Google Scholar 

  12. Knuth, D. The Art of Computer Programming, Volume2: Seminumerical Algorithms. Reading, MA: Addison-Wesley, 3rd ed., 1998.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Hellenic Aerospace Industry & Hellenic Open University, Rodu 2, Ano Iliupolis, 16342, Athens, Greece

    D.A. Karras

  2. Data Protection Authority, Omirou 8, 10564, Athens, Greece

    V. Zorkadis

Authors
  1. D.A. Karras
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. V. Zorkadis
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Australian Defence Force Academy, University of New South Wales, ACT 2600, Canberra, Australia

    Bob McKay

  2. Computer Science Laboratory, Australian National University, RSISE Building, ACT 0200, Canberra, Australia

    John Slaney

Rights and permissions

Reprints and permissions

Copyright information

© 2002 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Karras, D., Zorkadis, V. (2002). Strong Pseudorandom Bit Sequence Generators Using Neural Network Techniques and Their Evaluation for Secure Communications. In: McKay, B., Slaney, J. (eds) AI 2002: Advances in Artificial Intelligence. AI 2002. Lecture Notes in Computer Science(), vol 2557. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36187-1_54

Download citation

  • DOI: https://doi.org/10.1007/3-540-36187-1_54

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-00197-3

  • Online ISBN: 978-3-540-36187-9

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation