JOURNAL ARTICLE

Constructing a Nondegenerate m-Dimensional Integer-Domain Chaotic Map Model over GF(2n) with Application in PRNG.

  • Published In: International Journal of Bifurcation & Chaos in Applied Sciences & Engineering, 2024, v. 34, n. 13. P. 1 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Xu, Dongya; Liu, Hongjun 3 of 3

Abstract

To address the problem of dynamic degradation over a finite-precision platform of chaotic maps and the reversibility of linear chaotic maps, we propose an improved model over GF( 2 n ) that is called the nondegenerate m-Dimensional (m ≥ 2) Integer-Domain Chaotic Maps (mD-IDCMs). This model incorporates modular exponentiation operation, and is capable of constructing nondegenerate IDCMs of any dimension. Moreover, we prove the irreversibility of mD-IDCM and analyze its chaotic behaviors in terms of positive Lyapunov Exponents (LEs). The results of theoretical analysis show that the proposed mD-IDCM model can obtain the desired positive LEs by appropriately configuring its coefficient matrix. Then, we present two instances, and analyze their LEs, Kolmogorov entropy, Sample entropy, Correlation dimension, and the dynamic analysis indicates that the chaotic map constructed by mD-IDCM has ergodicity within a sufficiently large chaotic range. Finally, we design a Pseudo-Random Number Generator (PRNG) with a key to verify the practicability of the mD-IDCM. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:International Journal of Bifurcation & Chaos in Applied Sciences & Engineering. 2024/10, Vol. 34, Issue 13, p1
  • Document Type:Article
  • Subject Area:Mathematics
  • Publication Date:2024
  • ISSN:0218-1274
  • DOI:10.1142/S0218127424501608
  • Accession Number:180553894
  • Copyright Statement:Copyright of International Journal of Bifurcation & Chaos in Applied Sciences & Engineering is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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