A Learned Polyalphabetic Decryption Cipher SNE Simulation Notes Europe.pdf (422.67 kB)
A Learned Polyalphabetic Decryption Cipher
journal contribution
posted on 2022-05-19, 09:47 authored by Chaminda Hewage, Ambikesh Jayal, Glenn Jenkins, Ryan J. BrownThis 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 EuropePublisher
Argesim/TU WienVersion
- 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.28.4.1044Electronic ISSN
2306-0271Cardiff Met Affiliation
- Cardiff School of Technologies
Cardiff Met Authors
Chaminda Hewage Glenn JenkinsCopyright Holder
- © The Publisher
Language
- en