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Constructing Highly Nonlinear Cryptographic Balanced Boolean Functions on Learning Capabilities of Recurrent Neural Networks
- Source :
- IEEE Access, Vol 12, Pp 150255-150267 (2024)
- Publication Year :
- 2024
- Publisher :
- IEEE, 2024.
-
Abstract
- This study presents a novel approach to cryptographic algorithm design that harnesses the power of recurrent neural networks. Unlike traditional mathematical-based methods, neural networks offer nonlinear models that excel at capturing chaotic behavior within systems. We employ a recurrent neural network trained on Monte Carlo estimation to predict future states and generate confusion components. The resulting highly nonlinear substitution boxes exhibit exceptional characteristics, with a maximum nonlinearity of 114 and low linear and differential probabilities. To evaluate the efficacy of our methodology, we employ a comprehensive range of traditional and advanced metrics for assessing randomness and cryptanalytics. Comparative analysis against state-of-the-art methods demonstrates that our developed nonlinear confusion component offers remarkable efficiency for block-cipher applications.
Details
- Language :
- English
- ISSN :
- 21693536
- Volume :
- 12
- Database :
- Directory of Open Access Journals
- Journal :
- IEEE Access
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.29cf6d753a3d4e678a172e2ebd86cdf1
- Document Type :
- article
- Full Text :
- https://doi.org/10.1109/ACCESS.2024.3477260