NMRMG: Nonlinear Multiple-Recursive Matrix Generator Design Approaches and Its Randomness Analysis
Pages 577 - 597
Abstract
Highly random binary sequences generated by any keystream generator provides adequate service for the rapidly growing demands of cryptographic applications. Using several statistical randomness test analysis, we found that the randomness properties of binary sequences generated by multiple-recursive matrix generators are not statistically secure for cryptographic solutions. To overcome the randomness loopholes, we propose nonlinearly filtered multiple-recursive matrix generator and experimentally establish that the mentioned generator provides high-quality randomness results. We evaluate the statistical security of the proposed scheme with the help of NIST randomness test, autocorrelation test, linear complexity test, and avalanche test. Moreover, we compare our randomness test results with some of the high-quality software oriented stream ciphers like Snow, HC-128, and ZUC.
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© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022.
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Kluwer Academic Publishers
United States
Publication History
Published: 01 July 2022
Accepted: 29 January 2022
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