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

2022/03/16 by Servio Paguada, Paguada, Servio, Lejla Batina +5
Computer Science · Engineering · #Advanced Malware Detection Techniques #Cryptographic Implementations and Security #Cryptography and Security (cs.CR) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Semiconductor materials and devices #cs.CR #cs.LG

paper · pdf · doi:10.48550/arxiv.2203.08448

openalex publication_date 2022/03/16 · arxiv created 2022/03/17 · arxiv updated 2022/03/18 · openalex created_date 2022/04/03 · openalex updated_date 2026/07/28

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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