Publication Details
Comparison of Genetic Programming Methods on Design of Cryptographic Boolean Functions
Genetic Programming, Cartesian Genetic Programming, Linear Genetic Programming,
Cryptographic Boolean functions, Comparative Study.
The ever-increasing need for information security requires a constant refinement
of contemporary ciphers. One of these are stream ciphers which secure data by
utilizing a pseudo-randomly generated binary sequence. Generating
a cryptographically secure sequence is not an easy task and requires a Boolean
function possessing multiple cryptographic properties. One of the most successful
ways of designing these functions is genetic programming. In this paper, we
present a comparative study of three genetic programming methods, tree-based,
Cartesian and linear, on the task of generating Boolean functions with an even
number of inputs possessing good values of nonlinearity, balancedness,
correlation immunity, and algebraic degree. Our results provide a comprehensive
overview of how genetic programming methods compare when designing functions of
different sizes, and we show that linear genetic programming, which has not been
used for design of some of these functions before, is the best at dealing with
increasing number of inputs, and creates desired functions with better
reliability than the commonly used methods.
@inproceedings{BUT157181,
author="Jakub {Husa}",
title="Comparison of Genetic Programming Methods on Design of Cryptographic Boolean Functions",
booktitle="Genetic Programming 22st European Conference, EuroGP 2019, Proceedings",
year="2019",
pages="228--244",
publisher="Springer International Publishing",
address="Cham",
doi="10.1007/978-3-030-16670-0\{_}15",
isbn="978-3-030-14811-9",
url="https://www.fit.vut.cz/research/publication/11918/"
}