Profiled Side-Channel Analysis in the Efficient Attacker Framework

Conference Paper (2022)
Author(s)

Stjepan Picek (TU Delft - Cyber Security)

Annelie Heuser (Université de Rennes)

Guilherme Perin (TU Delft - Cyber Security)

Sylvain Guilley (Secure-IC SAS)

Research Group
Cyber Security
Copyright
© 2022 S. Picek, Annelie Heuser, G. Perin, Sylvain Guilley
DOI related publication
https://doi.org/10.1007/978-3-030-97348-3_3
More Info
expand_more
Publication Year
2022
Language
English
Copyright
© 2022 S. Picek, Annelie Heuser, G. Perin, Sylvain Guilley
Research Group
Cyber Security
Bibliographical Note
Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public. @en
Volume number
13173
Pages (from-to)
44-63
ISBN (print)
978-3-030-97347-6
ISBN (electronic)
978-3-030-97348-3
Reuse Rights

Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.

Abstract

Profiled side-channel attacks represent the most powerful category of side-channel attacks. There, the attacker has access to a clone device to profile its leaking behavior. Additionally, it is common to consider the attacker unbounded in power to allow the worst-case security analysis. This paper starts with a different premise where we are interested in the minimum power that the attacker requires to conduct a successful attack. We propose a new framework for profiled side-channel analysis that we call the Efficient Attacker Framework. With it, we require attacks to be as powerful as possible, but we also provide a setting that inherently allows a more objective analysis among attacks. To confirm our theoretical results, we provide an experimental evaluation of our framework in the context of deep learning-based side-channel analysis.

Files

Picek2022_Chapter_ProfiledSide... (pdf)
(pdf | 3.16 Mb)
- Embargo expired in 01-07-2023
License info not available