Comparative Analysis of Statistical Test Based on Data Compression Methods and Standard Tests for Assessing Randomness of Random Number Generators

This paper presents a detailed comparative analysis of statistical tests utilizing both modern data compressors and standard statistical methods for assessing the randomness of Ran-dom number generators(RNG). Our study aims to thoroughly evaluate the efficiency and performance of these tests in dete...

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Vydáno v:2024 IEEE International Multi-Conference on Engineering, Computer and Information Sciences (SIBIRCON) s. 19 - 24
Hlavní autor: Lulu, Yeshewas Getachew
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 30.09.2024
Témata:
Compressors
Computer security
Data compression
Data compression statistical tests
Generators
National Institute of Standards and Technology statistical test
NIST
random num-ber generator
Reliability
Statistical analysis
statistical tests
Systems modeling
Testing
TestU01 test
ISSN:2995-0996
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Shrnutí:This paper presents a detailed comparative analysis of statistical tests utilizing both modern data compressors and standard statistical methods for assessing the randomness of Ran-dom number generators(RNG). Our study aims to thoroughly evaluate the efficiency and performance of these tests in determining the quality of Random number generators output sequences. Data compression techniques have long been recognized as effective statistical tests, with some being asymptotically optimal. We compare the effectiveness of these data compressor-based tests with traditional statistical tests in assessing the randomness properties of Random number generators. Through rigorous experimentation and analysis conducted in this study, four weak and three strong generators were examined with Various file lengths 1 KB, 10 KB, 100 KB and 1 MB with 100 sequences each were utilized. Our results demonstrate that the efficiency of data compressor tests and standard statistical tests is closely similar. we show that both approaches yield comparable results in evaluating the randomness of Random number generators.
ISSN:2995-0996
DOI:10.1109/SIBIRCON63777.2024.10758518