Paper 2024/1300
SoK: 5 Years of Neural Differential Cryptanalysis
, Technology Innovation Institute, Technology Innovation Institute, TU Darmstadt, Radboud University NijmegenAbstract
At CRYPTO 2019, A. Gohr introduced Neural Differential Cryptanalysis by applying deep learning to modern block cipher cryptanalysis. Surprisingly, the resulting neural differential distinguishers enabled a new state-of-the-art key recovery complexity for 11 rounds of SPECK32. As of May 2024, according to Google Scholar, Gohr’s article has been cited 178 times. The wide variety of targets, techniques, settings, and evaluation methodologies that appear in these follow-up works grants a careful systematization of knowledge, which we provide in this paper. More specifically, we propose a taxonomy of these 178 publications and focus on the 50 that deal with differential neural distinguishers to systematically review and compare them. We then discuss two challenges for the field, namely comparability of neural distinguishers and scaling.
Metadata
- Available format(s)
-
PDF
- Category
- Attacks and cryptanalysis
- Publication info
- Preprint.
- Keywords
- Neural Differential CryptanalysisSystematization of Knowledge
- Contact author(s)
-
david gerault @ tii ae
anna hambitzer @ tii ae
moritz huppert @ tu-darmstadt de
stjepan picek @ ru nl - History
- 2024-08-23: approved
- 2024-08-20: received
- See all versions
- Short URL
- https://ia.cr/2024/1300
- License
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CC BY
BibTeX
@misc{cryptoeprint:2024/1300,
author = {David Gerault and Anna Hambitzer and Moritz Huppert and Stjepan Picek},
title = {{SoK}: 5 Years of Neural Differential Cryptanalysis},
howpublished = {Cryptology {ePrint} Archive, Paper 2024/1300},
year = {2024},
url = {https://eprint.iacr.org/2024/1300}
}
