mlsanityData to 14 Oct 2026

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

arXiv 2309.13170 ↗First posted 22 Sep 2023cs.CR cs.AIcited by 0

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Abstract (arXiv)

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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