Paper 2024/1300
SoK: 6 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 and used deep learning to improve the state-of-the-art cryptanalysis of 11-round SPECK32. As of February 2025, according to Google Scholar, Gohr’s article has been cited 229 times. The variety of targeted cryptographic primitives, 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 229 publications and systematically review the 66 papers focusing on neural differential distinguishers, pointing out promising directions. We then highlight future challenges in the field, particularly the need for improved comparability of neural distinguishers and advancements in 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
- 2025-04-24: revised
- 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}: 6 Years of Neural Differential Cryptanalysis},
howpublished = {Cryptology {ePrint} Archive, Paper 2024/1300},
year = {2024},
url = {https://eprint.iacr.org/2024/1300}
}
