Konferensartikel

Of Ciphers and Neurons – Detecting the Type of Ciphers Using Artificial Neural Networks

Nils Kopal
University of Siegen, Germany

Ladda ner artikelhttps://doi.org/10.3384/ecp2020171011

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

Linköping Electronic Conference Proceedings 171:11, s. 77-86

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Publicerad: 2020-05-19

ISBN: 978-91-7929-827-2

ISSN: 1650-3686 (tryckt), 1650-3740 (online)

Abstract

There are many (historical) unsolved ci-phertexts 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 us-ing different (statistical) methods. This can be dif?cult, since a multitude of ci-pher types exist. To help cryptanalysts, we developed a ?rst version of an arti?-cial neural network that is right now able to differentiate between ?ve classical ci-phers: simple monoalphabetic substitu-tion, Vigen`ere, Playfair, Hill, and transpo-sition. 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 detect-ing the cipher type. We tried to classify all ciphers of a new MysteryTwister C3 chal-lenge called “Cipher ID” created by Stamp in 2019. The network is able to classify about 90% of the ciphertexts of the chal-lenge correctly. Furthermore, the paper presents the current state-of-the-art of ci-pher type detection. Finally, we present a method which shows that one can save about 54% computation time for classi?-cation of cipher types when using our arti-?cial neural network instead of trying dif-ferent solvers for all ciphertext messages of Stamp’s challenge.

Nyckelord

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

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