A Learned Polyalphabetic Decryption Cipher SNE Simulation Notes Europe.pdf (422.67 kB)
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A Learned Polyalphabetic Decryption Cipher

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journal contribution
posted on 19.05.2022, 09:47 authored by Chaminda Hewage, Ambikesh Jayal, Glenn Jenkins, Ryan J. Brown

This paper examines the use of machine learning algorithms to model polyalphabetic ciphers for decryption. The focus of this research is to train and evaluate different machine learning algorithms to model the polyalphabetic cipher. The algorithms that have been selected are: (1) hill climbing; (2) genetic algorithm; (3) simulated annealing; and (4), random optimisation. The resulting models were deployed in a simulation to decrypt sample codes. The resulting analysis showed that the genetic algorithm was the most effective technique used in with hill climbing as second. Furthermore, both have the potential to be useful for larger problems. 

History

Published in

Simulation Notes Europe

Publisher

Argesim/TU Wien

Version

VoR (Version of Record)

Citation

Hewage, C., Jayal, A., Jenkins, G., Brown, R.J. (2018) 'A Learned Polyalphabetic Decryption Cipher', Simulation Notes Europe 28 (4). doi:10.11128/sne.28.4.1044

Electronic ISSN

2306-0271

Cardiff Met Affiliation

  • Cardiff School of Technologies

Cardiff Met Authors

Chaminda Hewage Glenn Jenkins

Copyright Holder

© The Publisher

Language

en

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