Discovering Non-Linear Boolean Functions by Evolving Walsh Transforms with Genetic Programming

Discovering Non-Linear Boolean Functions by Evolving Walsh Transforms with Genetic Programming

Stream ciphers usually rely on highly secure Boolean functions to ensure safe communication within unsafe channels. However, discovering secure Boolean functions is a non-trivial optimization problem that has been addressed by many optimization techniques: in particular by evolutionary algorithms. W...

Full description

Bibliographic Details
Main Authors: Luigi Rovito, Andrea De Lorenzo, Luca Manzoni
Format: Article
Language:English
Published: MDPI AG 2023-10-01
Series:Algorithms
Subjects:
artificial intelligence
cybersecurity
cryptography
evolutionary computation
evolutionary algorithms
genetic programming
Online Access:https://www.mdpi.com/1999-4893/16/11/499

Internet

https://www.mdpi.com/1999-4893/16/11/499

Similar Items