Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Research › peer-review
Comparative Analysis of Statistical Test Based on Data Compression Methods and Standard Tests for Assessing Randomness of Random Number Generators. / Lulu, Yeshewas Getachew.
2024 IEEE International Multi-Conference on Engineering, Computer and Information Sciences, SIBIRCON 2024. Institute of Electrical and Electronics Engineers Inc., 2024. p. 19-24 (2024 IEEE International Multi-Conference on Engineering, Computer and Information Sciences, SIBIRCON 2024).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Research › peer-review
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TY - GEN
T1 - Comparative Analysis of Statistical Test Based on Data Compression Methods and Standard Tests for Assessing Randomness of Random Number Generators
AU - Lulu, Yeshewas Getachew
PY - 2024/11/26
Y1 - 2024/11/26
N2 - 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.
AB - 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.
KW - Data compression statistical tests
KW - National Institute of Standards and Technology statistical test
KW - TestU01 test
KW - random num-ber generator
KW - statistical tests
UR - https://www.scopus.com/record/display.uri?eid=2-s2.0-85212159903&origin=inward&txGid=4741b882c07cc6b4fd56fc6bb3ddf323
UR - https://www.mendeley.com/catalogue/9cbd3295-e6b5-3861-8403-bbce69192d53/
U2 - 10.1109/SIBIRCON63777.2024.10758518
DO - 10.1109/SIBIRCON63777.2024.10758518
M3 - Conference contribution
SN - 9798331532024
T3 - 2024 IEEE International Multi-Conference on Engineering, Computer and Information Sciences, SIBIRCON 2024
SP - 19
EP - 24
BT - 2024 IEEE International Multi-Conference on Engineering, Computer and Information Sciences, SIBIRCON 2024
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2024 IEEE International Multi-Conference on Engineering, Computer and Information Sciences
Y2 - 30 September 2024 through 2 November 2024
ER -
ID: 61787512