Investigating Efficient Deep Learning Architectures For Side-Channel Attacks on AES

• URL: http://arxiv.org/abs/2309.13170v1
• Date: Fri, 22 Sep 2023 20:16:40 GMT
• Title: Investigating Efficient Deep Learning Architectures For Side-Channel Attacks on AES
• Authors: Yoha\"i-Eliel Berreby, Laurent Sauvage
• Abstract summary: We focus on the ANSSI Side-Channel Attack Database (ASCAD), and produce a JAX-based framework for deep-learning-based SCA. We also investigate the effectiveness of various Transformer-based models.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Over the past few years, deep learning has been getting progressively more popular for the exploitation of side-channel vulnerabilities in embedded cryptographic applications, as it offers advantages in terms of the amount of attack traces required for effective key recovery. A number of effective attacks using neural networks have already been published, but reducing their cost in terms of the amount of computing resources and data required is an ever-present goal, which we pursue in this work. We focus on the ANSSI Side-Channel Attack Database (ASCAD), and produce a JAX-based framework for deep-learning-based SCA, with which we reproduce a selection of previous results and build upon them in an attempt to improve their performance. We also investigate the effectiveness of various Transformer-based models.

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