Analysis of the pseudorandom number generators by the metrological characteristics

Authors

DOI:

https://doi.org/10.15587/1729-4061.2016.60608

Keywords:

pseudorandom number sequence generator, metrological characteristics of realizations, degree of conformity of generator

Abstract

The paper considers the method of checking the statistical conformity of the characteristics of realizations of noise signals with characteristics of uniform distribution law. The degree of conformity of realizations obtained from pseudorandom number sequence generators was checked by metrological characteristics. The conclusion on the generator usefulness was based on Pareto-optimal solutions for a multi-objective problem. The pilot study was conducted in the Matlab environment. The Martin method, congruent method and environment built-in generator were used as the pseudorandom number sequence generators. The research results showed that when using the Pareto-optimal solutions for the multi-objective problem of statistical conformity of metrological characteristics of realizations of white noise with the uniform distribution law for small volume samples (up to 5000 items), the generator built in the Matlab environment (function unifrnd) has a higher degree of conformity of realizations. However, when using the realizations of the white noise of larger volume (over 5000 items), the congruent method for pseudorandom number sequence generation becomes more significant. The Martin method has not proved as the best by the metrological characteristics for any sample volume.

Author Biographies

Ганна Вадимівна Мартинюк, National Aviation University 1 Komarova ave., Kyiv, Ukraine, 03058

Assistant

Department of information measuring systems

Юрій Юрійович Оникієнко, National Aviation University 1 Komarova ave., Kyiv, Ukraine, 03058

PhD

Department of Biocybernetics and aerospace medicine

Леонід Миколайович Щербак, National Aviation University 1 Komarova ave., Kyiv, Ukraine, 03058

Doctor of Technical Sciences, Professor

Department of information measuring systems

References

  1. Prokhorov, S. A. (2001). The mathematical description and modeling of random processes. Samara: Samara State. Aerospace University Press, 209.
  2. Martyniuk, G. V., Shcherbak, L. M. (2015). Statistical analysis of correlation characteristics of pseudorandom noise signals. Bulletin of the Academy of Engineering Sciences, 2, 101–105.
  3. Ivanov, M. A., Chuhunkov, I. V. (2003). Theory, application and evaluation of quality pseudorandom sequence generators. Moscow: KUDYTS-OBRAZ, 240.
  4. Random number generation. Available at: http://mandala.co.uk/links/random//
  5. Entacher, K. (2000). A collection of classical pseudorandom number generators with linear structures – advanced version. Availabe at: http://random.mat.sbg.ac.at/results/karl/server/server.html
  6. Gentle, E. (2005). Random Number Generation and Monte-Carlo Methods, 2nd. ed. Springer, 397. doi: 10.1007/b97336
  7. Ryabko, B. Y., Monarev, V. A. (2005). Using information theory approach to randomness testing. Journal of Statistical Planning and Inference, 133 (1), 95–110. doi: 10.1016/j.jspi.2004.02.010
  8. Marsaglia, G. DIEHARD Statistical Tests. Available at: http://stat.fsu.edu/~geo/diehard.html
  9. Soto, J. (1999). Randomness Testing of the Advanced Encryption Algorithms. NIST.
  10. Rukhin, A. (2001). A statistical test suite for random and pseudorandom number generators for cryptographic applications. NIST. Available at: http://csrc.nist.gov/publications/nistpubs/800-22-rev1a/SP800-22rev1a.pdf
  11. National Institute of Standards and Technology, “FIPS-197: Advanced Encryption Standard.” Available at: http://csrc.nist.gov/publications/fips/ fips197/fips-197.pdf
  12. L'Ecuyer, P., Simard, R. (2007). TestU01: A C Library for empirical testing of random number generators. ACM Transactions on Mathematical Software, 33 (4), 22. doi: 10.1145/1268776.1268777
  13. Mityankina, T. V., Shwidkiy, V. V., Szczerba A. I., Mityankin, M. A. (2009). Assessment of the quality of random number generators. Journal of Cherkasy State Technological University, 1, 41–46.
  14. Sokolovska, G. V. (2013). Statistical analysis of pseudorandom sequence generator is programmed in Matlab and Mathcad. Modeling and information technologies, 66, 26–30.
  15. Kuznetsov, A. A., Korolev, R. V., Ryabukha, Yu. N. (2008). Research of the statistical security pseudorandom number generators. Information processing systems, 3 (70), 79–82.
  16. Kazakova, N. F. (2010). Phased testing and selection of the constituent elements of of pseudorandom sequence generators. Eastern-European Journal of Enterprise Technologies, 2/8 (44), 44–48. Available at: http://journals.uran.ua/eejet/article/view/2734/2540
  17. Azhmuhamedov, M., Kolesova, N. A. (2010). Methodology to evaluate the quality of a sequence of random numbers. Bulletin ASTU. Ser: Management, Computer Science and Informatics, 2, 141–148.
  18. Wilkes, S. (1967). Mathematical statistics. Moscow: Nauka, 632.
  19. Wentzel, E. S. (1988). Operations research: tasks, principles, methodology. 2nd edition. Moscow: Nauka, 208.

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Published

2016-02-27

How to Cite

Мартинюк, Г. В., Оникієнко, Ю. Ю., & Щербак, Л. М. (2016). Analysis of the pseudorandom number generators by the metrological characteristics. Eastern-European Journal of Enterprise Technologies, 1(9(79), 25–30. https://doi.org/10.15587/1729-4061.2016.60608

Issue

Vol. 1 No. 9(79) (2016): Information and controlling system

Section

Information and controlling system

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Copyright (c) 2016 Леонід Миколайович Щербак, Ганна Вадимівна Мартинюк, Юрій Юрійович Оникієнко

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