SNE Volume 28-4 (2018)

Special Issue EUROSIM Promotion

Institut: Fakultät für Mathematik und Geoinformation
Autor: Felix Breitenecker
ISBN: 9783903024779
Seitenanzahl: 94
Herausgeber: TU Verlag
Erscheinungsort: Wien 22.00002

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Art.Nr. SNE 28/4

SNE Volume 28-4 (2018)

SNE Volume 28-4 (2018)
Special Issue EUROSIM Promotion

Institut: Fakultät für Mathematik und Geoinformation
Autor: Felix Breitenecker
ISBN: 9783903024779
Seitenanzahl: 94
Herausgeber: TU Verlag
Erscheinungsort: Wien


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.

Felix Breitenecker


Felix Breitenecker
SNE Editor-in-Chief

 

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