Playing with blocks: Toward re-usable deep learning models for side-channel profiled attacks

This paper introduces a deep learning modular network for side-channel analysis. Our deep learning approach features the capability to exchange part of it (modules) with others networks. We aim to introduce reusable trained modules into side-channel analysis instead of building architectures for eac...

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Bibliographic Details
Main Authors Paguada, Servio, Batina, Lejla, Buhan, Ileana, Armendariz, Igor
Format Journal Article
LanguageEnglish
Published 16.03.2022
Subjects
Computer Science - Cryptography and Security
Computer Science - Learning
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Summary:This paper introduces a deep learning modular network for side-channel analysis. Our deep learning approach features the capability to exchange part of it (modules) with others networks. We aim to introduce reusable trained modules into side-channel analysis instead of building architectures for each evaluation, reducing the body of work when conducting those. Our experiments demonstrate that our architecture feasibly assesses a side-channel evaluation suggesting that learning transferability is possible with the network we propose in this paper.
DOI:10.48550/arxiv.2203.08448