Mark Stamp
Department of Computer Science, San Jose State University, San Jose, California, USA
Fabio Di Troia
Department of Computer Science, San Jose State University, San Jose, California, USA
Miles Stamp
Los Gatos High School, Los Gatos, California, USA
Jasper Huang
Lynbrook High School, San Jose, California, USA
Ladda ner artikel
Ingår i: Proceedings of the 1st International Conference on Historical Cryptology HistoCrypt 2018
Linköping Electronic Conference Proceedings 149:11, s. 39-46
NEALT Proceedings Series 34:11, p. 39-46
Publicerad: 2018-06-13
ISBN: 978-91-7685-252-1
ISSN: 1650-3686 (tryckt), 1650-3740 (online)
Previous work has shown that hidden Markov models (HMM) can be effective for the cryptanalysis of simple substitution and homophonic substitution ciphers. Although computationally expensive, an HMM-based attack that employs multiple random restarts can offer a significant improvement over classic cryptanalysis techniques, in the sense of requiring less ciphertext to recover the key. In this paper, we show that HMMs are also applicable to the cryptanalysis of the well-known Vigenère cipher. We compare and contrast our HMM-based approach to recent research that uses Vigenère cryptanalysis to supposedly illustrate the strength of a type of neural network known as a generative adversarial network (GAN). In the context of Vigenère cryptanalysis, we show that an HMM can succeed with much less ciphertext than a GAN, and we argue that the model generated by an HMM is considerably more informative than that produced by a GAN.
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