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A new method for detecting binary random sequences in cryptography: First interval test for non-overlapping templates

Published: 20 December 2022 Publication History

Abstract

ABSTRACT: In the application of various fields of cryptography, the generation of the key should be random, because it directly determines the security of the system, so the detection of the random generation of the key is a crucial work in cryptography. Different detection methods detect different properties of sequences. The first interval test of non-overlapping templates introduced in this paper is mainly aimed at testing the frequency of the first interval of the specified template in the sequence. When the frequency of the first interval of the specified template conforms to the ideal distribution, the randomness hypothesis is accepted. On the contrary, the specific frequency the template, random hypothesis is rejected. This method focuses more on specifying the interval frequency of the occurrence of the template than some methods of checking the frequency of the occurrence of the template in the whole sequence. In addition, the sequence length examined by this detection method can be short or long. Compared with the random number detection method, the method introduced in this paper is more inclusive.

References

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Marsaglia, G.: The Marsaglia Random Number CD-ROM Including the DieHard Battery of Test of Randomness. http://stat.fsu.edu/pub/diehard/David Harel. 1979. First-Order Dynamic Logic. Lecture Notes in Computer Science, Vol. 68. Springer-Verlag, New York, NY. https://doi.org/10.1007/3-540-09237-4
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Li J, Lin S. New Short Binary Sequence Test Algorithm: First Interval Test[C]// CONF-CDS 2021: The 2nd International Conference on Computing and Data Science. 2021

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          CSSE '22: Proceedings of the 5th International Conference on Computer Science and Software Engineering
          October 2022
          753 pages
          ISBN:9781450397780
          DOI:10.1145/3569966
          Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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          Published: 20 December 2022

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