Preliminary study of applied binary neural networks for neural cryptography

Raul Horacio Valencia Tenorio, Chiu Wing Sham, Danilo Vasconcellos Vargas

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Adversarial neural cryptography is deemed as an encouraging area of research that could provide different perspective in the post-quantum cryptography age, specially for secure transmission of information. Nevertheless, it is still under explored with a handful of publications on the subject. This study proposes the theoretical implementation of a neuroevolved binary neural network based on boolean logic functions only (BiSUNA), with the purpose of encrypting/decrypting a payload between two agents, hiding information from a competitor.

Original languageEnglish
Title of host publicationGECCO 2020 Companion - Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion
PublisherAssociation for Computing Machinery, Inc
Pages291-292
Number of pages2
ISBN (Electronic)9781450371278
DOIs
Publication statusPublished - Jul 8 2020
Event2020 Genetic and Evolutionary Computation Conference, GECCO 2020 - Cancun, Mexico
Duration: Jul 8 2020Jul 12 2020

Publication series

NameGECCO 2020 Companion - Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion

Conference

Conference2020 Genetic and Evolutionary Computation Conference, GECCO 2020
CountryMexico
CityCancun
Period7/8/207/12/20

All Science Journal Classification (ASJC) codes

  • Computational Mathematics

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  • Cite this

    Tenorio, R. H. V., Sham, C. W., & Vargas, D. V. (2020). Preliminary study of applied binary neural networks for neural cryptography. In GECCO 2020 Companion - Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion (pp. 291-292). (GECCO 2020 Companion - Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion). Association for Computing Machinery, Inc. https://doi.org/10.1145/3377929.3389933