Comparison of Genetic Programming Methods on Design of Cryptographic Boolean Functions

Created by W.Langdon from gp-bibliography.bib Revision:1.7615

@InProceedings{Husa:2019:EuroGP,

• author = "Jakub Husa",
• title = "Comparison of Genetic Programming Methods on Design of Cryptographic {Boolean} Functions",
• booktitle = "EuroGP 2019: Proceedings of the 22nd European Conference on Genetic Programming",
• year = "2019",
• month = "24-26 " # apr,
• editor = "Lukas Sekanina and Ting Hu and Nuno Lourenco",
• series = "LNCS",
• volume = "11451",
• publisher = "Springer Verlag",
• address = "Leipzig, Germany",
• pages = "228--244",
• organisation = "EvoStar, Species",
• keywords = "genetic algorithms, genetic programming, Cartesian Genetic programming: Poster",
• isbn13 = "978-3-030-16669-4",
• URL = "https://www.springer.com/us/book/9783030166694",
• DOI = "doi:10.1007/978-3-030-16670-0_15",
• size = "16 pages",
• abstract = "The ever-increasing need for information security requires a constant refinement of contemporary ciphers. One of these are stream ciphers which secure data by using 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.",
• notes = "http://www.evostar.org/2019/cfp_eurogp.php#abstracts Part of \cite{Sekanina:2019:GP} EuroGP'2019 held in conjunction with EvoCOP2019, EvoMusArt2019 and EvoApplications2019",

}

Genetic Programming entries for Jakub Husa

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