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Evolving a Boolean Masked Adder Using Neuroevolution

  • The modular addition is a popular building block when designing lightweight ciphers. While algorithms mainly based on the addition can reach very high performance, masking their implementations results in a huge penalty. Since efficient protection against side-channel attacks is a requirement in lots of use cases, we focus on optimizing the Boolean masking of the modular addition. Contrary to recent related work, we target evolving a masked full adder instead of parts of a parallel prefix adder. We study how techniques typically found in neural network evolution and genetic algorithms can be adapted in order to help in evolving an efficiently masked adder. We customize a well-known neuroevolution algorithm, develop an optimized masked adder with our new approach and implement the ChaCha20 cipher on an ARM Cortex-M3 controller. We compare the performance of the protected neuroevolved implementation to solutions found by traditional search methods. Moreover, the leakage of our new solution is validated by a t-test conducted with a leakage simulator. We present under which circumstances our masked implementation outperforms related work and prove the feasibility of successfully using neuroevolution when searching for complex Boolean networks.

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Metadaten
Author:Sebastian Renner, Enrico Pozzobon, Jürgen MottokORCiDGND
DOI:https://doi.org/10.1007/978-3-031-21311-3_2
ISBN:978-3-031-21310-6
Parent Title (English):Attacks and Defenses for the Internet-of-Things, 5th International Workshop, ADIoT 2022: Copenhagen, Denmark, September 30, 2022, Revised Selected Papers
Publisher:Springer Nature
Place of publication:Cham
Editor:Wenjuan Li, Steven Furnell, Weizhi Meng
Document Type:conference proceeding (article)
Language:English
Year of first Publication:2022
Release Date:2022/12/14
First Page:21
Last Page:40
Andere Schriftenreihe:Lecture Notes in Computer Science ; 13745
Institutes:Fakultät Elektro- und Informationstechnik
Fakultät Elektro- und Informationstechnik / Laboratory for Safe and Secure Systems (LAS3)
research focus:Digitalisierung
Licence (German):Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG
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