SoK: Deep Learning-based Physical Side-channel Analysis

Stjepan Picek, Guilherme Perin, Luca Mariot, Lichao Wu, and Lejla Batina

Abstract

Side-channel attacks represent a realistic and serious threat to the security of embedded devices for almost three decades. The variety of attacks and targets they can be applied to have been introduced, and while the area of side-channel attacks and mitigations is very well-researched, it is yet to be consolidated. Deep learning-based side-channel attacks entered the field in recent years with the promise of more competitive performance and enlarged attackers' capabilities compared to other techniques. At the same time, the new attacks bring new challenges and complexities to the domain, making a systematization of the existing knowledge ever more necessary. In this SoK, we do exactly that, and by bringing new insights, we systematically structure the current knowledge of deep learning in side-channel analysis. We first dissect deep learning-assisted attacks into different phases and map those phases to the efforts conducted so far in the domain. For each of the phases, we identify the weaknesses and challenges that triggered the known open problems. We connect the attacks to the existing threat models and evaluate their advantages and drawbacks. We finish by discussing other threat models that should be investigated and propose directions for future works.

Available format(s)
Category
Foundations
Publication info
Preprint. MINOR revision.
Keywords
Side-channel attacksDeep learningProfiling attacksSupervised learning
Contact author(s)
picek stjepan @ gmail com
guilhermeperin7 @ gmail com
L Mariot @ tudelft nl
lichao wu9 @ gmail com
lejla @ cs ru nl
History
Short URL
https://ia.cr/2021/1092

CC BY

BibTeX

@misc{cryptoeprint:2021/1092,
author = {Stjepan Picek and Guilherme Perin and Luca Mariot and Lichao Wu and Lejla Batina},
title = {SoK: Deep Learning-based Physical Side-channel Analysis},
howpublished = {Cryptology ePrint Archive, Paper 2021/1092},
year = {2021},
note = {\url{https://eprint.iacr.org/2021/1092}},
url = {https://eprint.iacr.org/2021/1092}
}

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