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.
Author: | Sebastian Renner, Enrico Pozzobon, Jürgen MottokORCiDGND |
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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 |