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Playing With Blocks: Toward Re-Usable Deep Learning Models for Side-Channel Profiled Attacks

Paguada S., Batina L., Buhan I., Armendariz I.

IEEE Transactions on Information Forensics and Security


This paper introduces a deep learning modular network for side-channel analysis. Our approach features a deep learning architecture with the capability to exchange parts (modules) with other neural networks. We aim to introduce reusable trained modules into side-channel analysis instead of building architectures from scratch for each evaluation, reducing the body of work. Our experiments demonstrate that our architecture feasibly assesses a side-channel evaluation, suggesting that learning transferability is possible using the architecture we propose in this paper.

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