Of Ciphers and Neurons Detecting the Type of Ciphers Using Artificial Neural Networks Nils Kopal University of Siegen, Germany
[email protected] Abstract cuits). ANNs found usages in a broad set of dif- ferent applications and research fields. Their main There are many (historical) unsolved ci- purpose is fast filtering, classifying, and process- phertexts from which we don’t know the ing of (mostly) non-linear data, e.g. image pro- type of cipher which was used to encrypt cessing, speech recognition, and language trans- these. A first step each cryptanalyst does lation. Besides that, scientists were also able to is to try to identify their cipher types us- “teach” ANNs to play games or to create paintings ing different (statistical) methods. This in the style of famous artists. can be difficult, since a multitude of ci- Inspired by the vast growth of ANNs, also cryp- pher types exist. To help cryptanalysts, tologists started to use them for different crypto- we developed a first version of an artifi- graphic and cryptanalytic problems. Examples are cial neural network that is right now able the learning of complex cryptographic algorithms, to differentiate between five classical ci- e.g. the Enigma machine, or the detection of the phers: simple monoalphabetic substitu- type of cipher used for encrypting a specific ci- tion, Vigenere,` Playfair, Hill, and transpo- phertext. sition. The network is based on Google’s In late 2019 Stamp published a challenge on the TensorFlow library as well as Keras. This MysteryTwister C3 (MTC3) website called “Ci- paper presents the current progress in the pher ID”.