Article | Proceedings of the 3rd International Conference on Historical Cryptology HistoCrypt 2020 | Of Ciphers and Neurons – Detecting the Type of Ciphers Using Artificial Neural Networks Linköping University Electronic Press Conference Proceedings
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Title:
Of Ciphers and Neurons – Detecting the Type of Ciphers Using Artificial Neural Networks
Author:
Nils Kopal: University of Siegen, Germany
DOI:
https://doi.org/10.3384/ecp2020171011
Download:
Full text (pdf)
Year:
2020
Conference:
Proceedings of the 3rd International Conference on Historical Cryptology HistoCrypt 2020
Issue:
171
Article no.:
011
Pages:
77-86
No. of pages:
10
Publication type:
Abstract and Fulltext
Published:
2020-05-19
ISBN:
978-91-7929-827-2
Series:
Linköping Electronic Conference Proceedings
ISSN (print):
1650-3686
ISSN (online):
1650-3740
Publisher:
Linköping University Electronic Press, Linköpings universitet


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There are many (historical) unsolved ciphertexts from which we don’t know the type of cipher which was used to encrypt these. A ?rst step each cryptanalyst does is to try to identify their cipher types using different (statistical) methods. This can be difficult, since a multitude of cipher types exist. To help cryptanalysts, we developed a ?rst version of an artificial neural network that is right now able to differentiate between five classical ciphers: simple monoalphabetic substitution, Vigenère, Playfair, Hill, and transposition. The network is based on Google’s TensorFlow library as well as Keras. This paper presents the current progress in the research of using such networks for detecting the cipher type. We tried to classify all ciphers of a new MysteryTwister C3 challenge called “Cipher ID” created by Stamp in 2019. The network is able to classify about 90% of the ciphertexts of the challenge correctly. Furthermore, the paper presents the current state-of-the-art of cipher type detection. Finally, we present a method which shows that one can save about 54% computation time for classification of cipher types when using our artificial neural network instead of trying different solvers for all ciphertext messages of Stamp’s challenge.

Keywords: cipher type detection; artificial neural networks; challenge; cryptanalysis; machine learning; TensorfFlow; Keras; Python

Proceedings of the 3rd International Conference on Historical Cryptology HistoCrypt 2020

Author:
Nils Kopal
Title:
Of Ciphers and Neurons – Detecting the Type of Ciphers Using Artificial Neural Networks
DOI:
10.3384/ecp2020171011
References:
No references available

Proceedings of the 3rd International Conference on Historical Cryptology HistoCrypt 2020

Author:
Nils Kopal
Title:
Of Ciphers and Neurons – Detecting the Type of Ciphers Using Artificial Neural Networks
DOI:
https://doi.org10.3384/ecp2020171011
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Last updated: 2019-11-06