Cryptology ePrint Archive: Report 2020/757

Understanding Methodology for Efficient CNN Architectures in Profiling Attacks

Gabriel Zaid and Lilian Bossuet and 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].

Category / Keywords: secret-key cryptography / Side-Channel Attacks, Deep Learning, Network Architecture, Weight Visualization, Entanglement

Date: received 20 Jun 2020

Contact author: gabriel zaid at univ-st-etienne fr

Available format(s): PDF | BibTeX Citation

Version: 20200621:174244 (All versions of this report)

Short URL: ia.cr/2020/757


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