A Cipher-Agnostic Neural Training Pipeline with Automated Finding of Good Input Differences

Emanuele Bellini; David Gerault; Anna Hambitzer; Matteo Rossi

Paper 2022/1467

A Cipher-Agnostic Neural Training Pipeline with Automated Finding of Good Input Differences

Emanuele Bellini

, Technology Innovation Institute

David Gerault

, Technology Innovation Institute

Anna Hambitzer

, Technology Innovation Institute

Matteo Rossi

, Politecnico di Torino, Torino, Italy

Abstract

Neural cryptanalysis is the study of cryptographic primitives throughmachine learning techniques. Following Gohr’s seminal paper at CRYPTO 2019, afocus has been placed on improving the accuracy of such distinguishers against specific primitives, using dedicated training schemes, in order to obtain better key recovery attacks based on machine learning. These distinguishers are highly specialized and not trivially applicable to other primitives. In this paper, we focus on the opposite problem: building a generic pipeline for neural cryptanalysis. Our tool is composed of two parts. The first part is an evolutionary algorithm for the search of good input differences for neural distinguishers. The second part is DBitNet, a neuraldistinguisher architecture agnostic to the structure of the cipher. We show thatthis fully automated pipeline is competitive with a highly specialized approach, inparticular for SPECK32, and SIMON32. We provide new neural distinguishers forseveral primitives (XTEA, LEA, HIGHT, SIMON128, SPECK128) and improve overthe state-of-the-art for PRESENT, KATAN, TEA and GIMLI.

Metadata

Available format(s): PDF
Category: Attacks and cryptanalysis
Publication info: Published by the IACR in TOSC 2024
Keywords: Neural distinguishers
Contact author(s): emanuele bellini @ tii ae
david gerault @ tii ae
anna hambitzer @ tii ae
matteo rossi @ polito it
History: 2024-01-24: last of 4 revisions; 2022-10-26: received; See all versions
Short URL: https://ia.cr/2022/1467
License: CC BY

BibTeX

@misc{cryptoeprint:2022/1467,
      author = {Emanuele Bellini and David Gerault and Anna Hambitzer and Matteo Rossi},
      title = {A Cipher-Agnostic Neural Training Pipeline with Automated Finding of Good Input Differences},
      howpublished = {Cryptology ePrint Archive, Paper 2022/1467},
      year = {2022},
      note = {\url{https://eprint.iacr.org/2022/1467}},
      url = {https://eprint.iacr.org/2022/1467}
}