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Higher order side-channel attack resilient S-boxes

Authors:

Nikita Veshchikov,

Olivier MarkowitchAuthors Info & Claims

CF '18: Proceedings of the 15th ACM International Conference on Computing Frontiers

Pages 336 - 341

https://doi.org/10.1145/3203217.3206428

Published: 08 May 2018 Publication History

Abstract

Masking schemes represent a well-researched and successful option to follow when considering side-channel countermeasures. Still, such measures increase the implementation cost in terms of power consumption, clock cycles, and random numbers generation. In fact, the higher the order of protection against side-channel adversaries, the higher the implementation cost of countermeasures. S-boxes represent the most vulnerable part in an implementation when considering side-channel adversary. In this paper, we investigate how to generate S-boxes that have improved resilience against varying orders of side-channel attacks while having minimal implementation costs. We examine whether S-boxes generated against a certain order of attack also represent a good solution when considering different order of attacks. We demonstrate that we successfully generated S-boxes resilient against a certain physical attack order but the improvements are small. As a result, S-boxes that are resilient against first order attacks stay resilient against higher-order attacks, which saves computational power during the design of higher-order side-channel attacks resilient S-boxes.

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Cited By

Picek SJakobovic DWagner M(2022)Evolutionary computation and machine learning in securityProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3520304.3534087(1572-1601)Online publication date: 9-Jul-2022
https://dl.acm.org/doi/10.1145/3520304.3534087
Picek SJakobovic DKrawiec K(2021)Evolutionary computation and machine learning in cryptologyProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3449726.3461420(1089-1118)Online publication date: 7-Jul-2021
https://dl.acm.org/doi/10.1145/3449726.3461420
Picek SJakobovic DCoello Coello C(2020)Evolutionary computation and machine learning in cryptologyProceedings of the 2020 Genetic and Evolutionary Computation Conference Companion10.1145/3377929.3389886(1147-1173)Online publication date: 8-Jul-2020
https://dl.acm.org/doi/10.1145/3377929.3389886
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Published In

cover image ACM Conferences

CF '18: Proceedings of the 15th ACM International Conference on Computing Frontiers

May 2018

401 pages

ISBN:9781450357616

DOI:10.1145/3203217

General Chairs:
David Kaeli
Northeastern University
,
Miquel Pericàs
Chalmers University of Technology, SE

Copyright © 2018 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 08 May 2018

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May 8 - 10, 2018

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Cited By

Picek SJakobovic DWagner M(2022)Evolutionary computation and machine learning in securityProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3520304.3534087(1572-1601)Online publication date: 9-Jul-2022
https://dl.acm.org/doi/10.1145/3520304.3534087
Picek SJakobovic DKrawiec K(2021)Evolutionary computation and machine learning in cryptologyProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3449726.3461420(1089-1118)Online publication date: 7-Jul-2021
https://dl.acm.org/doi/10.1145/3449726.3461420
Picek SJakobovic DCoello Coello C(2020)Evolutionary computation and machine learning in cryptologyProceedings of the 2020 Genetic and Evolutionary Computation Conference Companion10.1145/3377929.3389886(1147-1173)Online publication date: 8-Jul-2020
https://dl.acm.org/doi/10.1145/3377929.3389886
Ouladj MGuillot PMokrane F(2019)Chosen message strategy to improve the correlation power analysisIET Information Security10.1049/iet-ifs.2018.510313:4(304-310)Online publication date: Jul-2019
https://doi.org/10.1049/iet-ifs.2018.5103

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