Detection of Classical Cipher Types with Feature-Learning Approaches

Ernst Leierzopf, Vasily Mikhalev, Nils Kopal, Bernhard Esslinger, Harald Lampesberger, Eckehard Hermann

Research output: Chapter in Book/Report/Conference proceedingsConference contributionpeer-review

Abstract

To break a ciphertext, as a first step, it is essential to identify the cipher used to produce the ciphertext. Cryptanalysis has acquired deep knowledge on cryptographic weaknesses of classical ciphers, and modern ciphers have been designed to circumvent these weaknesses. The American Cryptogram Association (ACA) standardized so-called classical ciphers, which had historical relevance up to World War II. Identifying these cipher types using machine learning has shown promising results, but the state of the art relies on engineered features based on cryptanalysis. To overcome this dependency on domain knowledge, we explore in this paper the applicability of the two feature-learning algorithms long short-term memory (LSTM) and Transformer, for 55 classical cipher types from ACA. To lower the necessary data and the training time, various transfer-learning scenarios are investigated. Over a dataset of 10 million ciphertexts with a text length of 100 characters, Transformer correctly identified 72.33% of the ciphers, which is a slightly worse result than the best feature-engineering approach. Furthermore, with an ensemble model of feature-engineering and feature-learning neural network types, 82.78% accuracy over the same dataset has been achieved, which is the best known result for this significant problem in the field of cryptanalysis.

Original languageEnglish
Title of host publicationData Mining - 19th Australasian Conference on Data Mining, AusDM, 2021, Proceedings
Subtitle of host publicationAusDM 2021
EditorsYue Xu, Rosalind Wang, Anton Lord, Yee Ling Boo, Richi Nayak, Yanchang Zhao, Graham Williams
PublisherSpringer
Pages152-164
Number of pages13
VolumeCommunications in Computer and Information Science
Edition1504
ISBN (Electronic)978-981-16-8531-6
ISBN (Print)978-981-16-8530-9
DOIs
Publication statusPublished - 2021

Publication series

NameCommunications in Computer and Information Science
Volume1504 CCIS
ISSN (Print)1865-0929
ISSN (Electronic)1865-0937

Keywords

  • Cipher-type detection
  • Classical ciphers
  • Ensemble learning
  • Neural networks
  • Transfer learning

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