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

Servio Paguada; Lejla Batina; Ileana Buhan; Igor Armendariz

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arXiv · 2203.08448

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

Servio Paguada·Lejla Batina·Ileana Buhan·Igor Armendariz

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Abstract

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.

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Servio Paguada, Lejla Batina, Ileana Buhan, Igor Armendariz. 2022-03-16. Playing with blocks: Toward re-usable deep learning models for side-channel profiled attacks. https://arxiv.org/abs/2203.08448

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