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Title:
Understanding Methodology for Efficient CNN Architectures in Profiling Attacks
Authors: Gabriel Zaid, Lilian Bossuet, Amaury Habrard, 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].
ePrint: https://eprint.iacr.org/2020/757
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