COMPARATIVE STUDY OF TESTS FOR ASSESSMENT OF STATISTICAL CHARACTERISTICS OF RANDOM AND PSEUDO-RANDOM SEQUENCE GENERATORS
DOI:
https://doi.org/10.28925/2663-4023.2024.24.115132Keywords:
informational security, generators of random sequences, generators of pseudorandom sequences, statistical tests, NIST, DieHard, TestU01Abstract
In the world of information security, computer science and cryptography, the issue of statistical security of generated sequences is very important. Statistical sequence security plays an important role in the following fields: cryptography, computer security, system modeling, statistical analysis, and information security in networks. This article is devoted to the study and comparison of test sets for evaluating the statistical properties of random and pseudorandom sequence generators. The comparison focuses on well-known test suites such as NIST, DieHard, and TestU01. These tests were selected for study because of their widespread use and recognized effectiveness in measuring the quality of generators. The article examines various aspects of these test suites, including purpose, complexity, scope, scoring accuracy, popularity, challenges and limitations, and innovation and development. NIST tests are widely used in cryptography and research, and they take a number of approaches to evaluate different aspects of random sequences. DieHard tests focus on complex statistical properties and are usually used for more in-depth analysis of generators. On the other hand, TestU01 tests have greater sensitivity and branching, allowing to detect a wider range of flaws in random number generators. A comparative study of the NIST, DieHard, and TestU01 tests revealed that each of them has its advantages and disadvantages in evaluating the statistical characteristics of generators. A detailed review of different test suites provides a better understanding of their strengths and limitations, which can help in choosing the right suite of tests for a particular task. The integrated use of these tests can provide a more accurate and complete assessment of the quality of generators. The obtained results will be a useful starting point for further research in this direction and in the development of reliable generators. The conclusions of the article may be useful for researchers, software developers, and other specialists who work with random and pseudo-random sequence generators.
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