In historical cryptology, cleartext represents text written in a known language ina cipher (a hand-written manuscript aiming at hiding the content of a message).Cleartext can give us an historical interpretation and contextualisation of themanuscript and could help researchers in cryptanalysis, but to these days thereis still no research on how to automatically detect cleartext and identifying itslanguage. In this paper, we investigate to what extent we can automaticallydistinguish cleartext from ciphertext in transcribed historical ciphers and towhat extent we are able to identify its language. We took a rule-based approachand run 7 different models using historical language models on ciphertextsprovided by the DECRYPT-Project. Our results show that using unigrams andbigrams on a word-level combined with 3-grams, 4-grams and 5-grams on acharacter-level is the best approach to tackle cleartext detection.

This thesis assesses the influence of language models on decryption of historical German ciphers. Previous research on language identification and cleartext detection indicates that it is beneficial to use historical language models (LM) while dealing with historical ciphers as they can outperform models trained on present-day data. To date, no systematic investigation has considered the impact of choosing different LMs for the decryption of ciphers. Therefore, we conducted a series of experiments with the aim of exploring this assumption. Using historical data from the HistCorp collection and Project Gutenberg, we have created 3-gram, 4-gram and 5-gram models, as well as constructed substitution ciphers for testing of the models. The results show that in most cases language models trained on historical data perform better than the larger modern models, while the most consistent results for the tested ciphers gave the 4-gram models.