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Investigating Efficient Deep Learning Architectures For Side-Channel Attacks on AES
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.Comment: 12 pages, 6 figures. This manuscript is a report produced as part of
a T\'el\'ecom Paris "PRIM" (Project Recherche et Innovation Master / Master's
Research and Innovation Project
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