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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Veröffentlicht in:	IEEE access 2024, Vol.12, p.150255-150267
Hauptverfasser:	Muhammad Waseem, Hafiz, Asfand Hafeez, Muhammad, Ahmad, Shabir, David Deebak, Bakkiam, Munir, Noor, Majeed, Abdul, Oun Hwang, Seoung
Format:	Artikel
Sprache:	eng
Schlagworte:	Approximation algorithms Block ciphers Boolean functions Ciphers confusion components Cryptography Estimation Monte Carlo estimation Monte Carlo methods Recurrent neural networks substitution boxes Time series analysis Training Vectors
Online-Zugang:	Volltext
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