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A Learned Polyalphabetic Decryption Cipher

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Author
Hewage, Chaminda
Jayal, Ambikesh
Jenkins, Glenn
Brown, Ryan J.
Date
2018-12-01
Acceptance date
2018-12-08
Type
Article
Publisher
TU Wien
ISSN
2306-0271
Metadata
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Abstract
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.
Journal/conference proceeding
Simulation Notes Europe;
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
URI
http://hdl.handle.net/10369/10345
DOI
https://doi.org/10.11128/sne.28.4.1044
Description
Article published in Simulation Notes Europe, available open access at https://doi.org/10.11128/sne.28.4.1044
Rights
http://www.rioxx.net/licenses/all-rights-reserved
Sponsorship
Cardiff Metropolitan University (Grant ID: Cardiff Metropolian (Internal))
Collections
  • School of Technologies Research [151]

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