Paper 2022/1521
An Assessment of Differential-Neural Distinguishers
Abstract
Since the introduction of differential-neural cryptanalysis, as the machine learning assisted differential cryptanalysis proposed in [Goh19] is coined by now, a lot of followup works have been published, showing the applicability for a wide variety of ciphers. In this work, we set out to vet a multitude of differential-neural distinguishers presented so far, and additionally provide general insights. Firstly, we show for a selection of different ciphers how differential-neural distinguishers for those ciphers can be (automatically) optimized, also providing guidance to do so for other ciphers as well. Secondly, we explore a correlation between a differential-neural distinguisher's accuracy and a standard notion of difference between the two underlying distributions. Furthermore, we show that for a whole (practically relevant) class of ciphers, the differential-neural distinguisher can use differential features only. At last, we also rectify a common mistake in current literature, and show that, making use of an idea already presented in the foundational work[Goh19], the claimed improvements from using multiple ciphertext-pairs at once are at most marginal, if not non-existent.
Metadata
- Available format(s)
-
PDF
- Category
- Attacks and cryptanalysis
- Publication info
- Preprint.
- Keywords
- Deep LearningDifferential Cryptanalysis SimonSpeckSkinnyPresentKatanChaCha
- Contact author(s)
-
aron gohr @ gmail com
gregor leander @ rub de
patrick neumann @ rub de - History
- 2022-11-07: approved
- 2022-11-03: received
- See all versions
- Short URL
- https://ia.cr/2022/1521
- License
-
CC BY
BibTeX
@misc{cryptoeprint:2022/1521,
author = {Aron Gohr and Gregor Leander and Patrick Neumann},
title = {An Assessment of Differential-Neural Distinguishers},
howpublished = {Cryptology {ePrint} Archive, Paper 2022/1521},
year = {2022},
url = {https://eprint.iacr.org/2022/1521}
}
