Cηι DAE: Cryptographically Distinguishing Autoencoder for Cipher Cryptanalysis

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Abstract

We propose a new autoencoder (AE) construction Cηι DAE (Cryptographically Distinguishing AE) based on a novel loss formulation to solve the cipher cryptanalysis distinguishing problem in the domain of cryptology. Vanilla AE and variational AE are unable to address this problem as they are designed to draw new samples which are either similar to the input sample or are from the same distribution. Such generated samples do not facilitate the cryptanalysis task. We show that our AE construction enables the discovery of cipher distinguishers, which are the fundamental building blocks that make or break new cipher design proposals. This also answers an open question on the applicability of autoencoders for cipher cryptanalysis; as to date, only discriminative models have been applied for cryptanalysis problems. To the best of our knowledge, Cηι DAE is the first-known generative model designed to solve crypt-analysis problems. We apply our Cηι DAE model to discover distinguishing properties for up to 10 rounds of the NSA-designed Speck32/64 cipher that allows to distinguish it from a random permutation. This contrasts with the best-known machine learning-discovered neural distinguisher in the literature that covers up to 8 rounds of Speck32/64. Unlike these recent related work which leverage on white box analysis and human-guided differential or linear analysis in order for machine learning models to be applicable, our Cηι DAE distinguisher does not require prior human cryptanalytic knowledge. This motivates the new direction of human-unsupervised machine learning-based cryptanalysis techniques.

Original languageEnglish
Title of host publicationGLOBECOM 2023 - 2023 IEEE Global Communications Conference
EditorsKhaled Ben Letaief
Place of PublicationPiscataway NJ USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages4467-4472
Number of pages6
ISBN (Electronic)9798350310900
ISBN (Print)9798350310917
DOIs
Publication statusPublished - 2023
EventIEEE Global Communications Conference 2023 - Kuala Lumpur, Malaysia
Duration: 4 Dec 20238 Dec 2023
https://ieeexplore.ieee.org/xpl/conhome/10436708/proceeding (Proceedings)
https://www.comsoc.org/conferences-events/ieee-global-communications-conference-2023 (Website)

Publication series

NameProceedings - IEEE Global Communications Conference, GLOBECOM
PublisherIEEE, Institute of Electrical and Electronics Engineers
ISSN (Print)2334-0983
ISSN (Electronic)2576-6813

Conference

ConferenceIEEE Global Communications Conference 2023
Abbreviated titleGLOBECOM 2023
Country/TerritoryMalaysia
CityKuala Lumpur
Period4/12/238/12/23
Internet address

Keywords

  • autoencoder
  • cipher
  • cryptanalysis
  • distinguisher
  • Generative model

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