Constructing Highly Nonlinear Cryptographic Balanced Boolean Functions on Learning Capabilities of Recurrent Neural Networks

Constructing Highly Nonlinear Cryptographic Balanced Boolean Functions on Learning Capabilities of Recurrent Neural Networks

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 ne...

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Bibliographic Details
Main Authors: Hafiz Muhammad Waseem, Muhammad Asfand Hafeez, Shabir Ahmad, Bakkiam David Deebak, Noor Munir, Abdul Majeed, Seoung Oun Hwang
Format: Article
Language:English
Published: IEEE 2024-01-01
Series:IEEE Access
Subjects:
Block ciphers
confusion components
Monte Carlo estimation
recurrent neural networks
substitution boxes
Online Access:https://ieeexplore.ieee.org/document/10710344/

Internet

https://ieeexplore.ieee.org/document/10710344/

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