Paper 2020/757
Understanding Methodology for Efficient CNN Architectures in Profiling Attacks
Gabriel Zaid, Lilian Bossuet, Amaury Habrard, and Alexandre Venelli
Abstract
The use of deep learning in side-channel analysis has been more and more prominent recently. In particular, Convolution Neural Networks (CNN) are very efficient tools to extract the secret information from side-channel traces. Previous work regarding the use of CNN in side-channel has been mostly proposed through practical results. Zaid et al. have proposed a theoretical methodology in order to better understand the convolutional part of CNN and to understand how to construct an efficient CNN in the side-channel context [ZBHV19]. The proposal of Zaid et al. has been recently questioned by [WAGP20]. However this revisit is based on wrong assumptions and misinterpretations. Hence, many of the claims of [WAGP20] are unfounded regarding [ZBHV19]. In this paper, we clear out the potential misunderstandings brought by [WAGP20] and explain more thoroughly the contributions of [ZBHV19].
Metadata
- Available format(s)
-
PDF
- Category
- Secret-key cryptography
- Publication info
- Preprint. MINOR revision.
- Keywords
- Side-Channel AttacksDeep LearningNetwork ArchitectureWeight VisualizationEntanglement
- Contact author(s)
- gabriel zaid @ univ-st-etienne fr
- History
- 2020-06-21: received
- Short URL
- https://ia.cr/2020/757
- License
-
CC BY
BibTeX
@misc{cryptoeprint:2020/757,
author = {Gabriel Zaid and Lilian Bossuet and Amaury Habrard and Alexandre Venelli},
title = {Understanding Methodology for Efficient {CNN} Architectures in Profiling Attacks},
howpublished = {Cryptology {ePrint} Archive, Paper 2020/757},
year = {2020},
url = {https://eprint.iacr.org/2020/757}
}
