COMPUTATION COMPLEXITY DECREASE IN MACHINE EXPERIMENTS AT VERIFICATION OF CRYPTOGRAPHIC ALGORITHMS
Abstract and keywords
Abstract (English):
The problem of obtaining a set of samples for the assessment of cryptographic algorithms quality on the basis of statistical tests use is considered. New properties of Markov binary chains taking into account dependences of probabilities of binary vectors with different length are described. The analytical expressions allowing the computation of dependences of range limits in values of probabilities of multidimen-sional binary random values upon probabilities of bi-nary random values with smaller dimension are of-fered. The reasons for the necessity of an additional procedure of rejection at the simulation of the realiza-tion of Markov binary processes are defined. A method for the directed search of probability values of sets in the distribution of Markov binary sequences allowing the generation of ergodic binary random sequences that allows refusing completely the procedure of rejection is considered. An algorithm realizing a mentioned method possessing a lowered computational complexity in comparison with the wellknown algorithms for the organization of a computational experiment on the investigation of statistical properties of binary random sequences is presented.

Keywords:
statistical tests, Markov chains, binary sequences (chains), simulation, probabilities of binary vectors, discrete random value, computational complexity, cryptographic algorithms
Text
Publication text (PDF): Read Download
References

1. Fomichyov, V. М. Methods of Discrete Mathematics in Cryptology /V.М. Fomichyov. - М.: Dialogue-MEPI, 2010. - pp. 424.

2. Gustafson. A computer package for measuring strength of encryption algorithms/Gustafson//Journal of Computers & Securi-ty. - 1994. - Vol. 13. - № 8. - P. 687-697.

3. Ritter, T. Randomness Tests and Related Topics/T. Ritter.-URL:http://www.ciphersbyritter.com/ RES/RANDTEST.HTM.

4. Buslenko, N.P. Method of Statistical Tests (Monte-Carlo) and Its Realization on Digital Comput-ers/N.P.Buslenko, Yu.А.Shreider. - М.: SPPML, 1961. - 226.

5. Barucha-Reed, А. Т. Elements of Theory of Markov Processes and Their Applications/А.Т. Barucha-Reed.-М.: Science, 1969. - pp. 512.

6. Yermakov, S.М. Statistical Modeling. Part. 1. Si-mulation of Distributions: manual/S.М. Yermakov. - S-Pb.: Smirnov RIM&M, 2006.-pp. 63.

7. Keipers, L. Uniform Distribution of Sequences /L. Keipers, G. Niderreiter.-М.: Science, 1985. - pp. 408.

8. Sovetov, B.Ya. System Modeling / B.Ya. Sovetov, S.А. Yakovlev.-М.: Yuright, 2012. - pp. 343.

9. Bliznyuk, V.I. Method of directed search of distribution sets in problems of simulation of Markov bi-nary sequences / V.I. Bliznyuk, М.Yu. Konyshev, V.А. Ivanov, S.V. Kharchenko //Industrial ASC and Controllers. 2015. - №5. pp. 40 45.

Login or Create
* Forgot password?