1. Constructing Highly Nonlinear Cryptographic Balanced Boolean Functions on Learning Capabilities of Recurrent Neural Networks
- Author
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Hafiz Muhammad Waseem, Muhammad Asfand Hafeez, Shabir Ahmad, Bakkiam David Deebak, Noor Munir, Abdul Majeed, and Seoung Oun Hwang
- Subjects
Block ciphers ,confusion components ,Monte Carlo estimation ,recurrent neural networks ,substitution boxes ,Electrical engineering. Electronics. Nuclear engineering ,TK1-9971 - 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.
- Published
- 2024
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