Paper 2025/1135 Original: eprint.iacr.org/2025/1135 ↗
Keep It Unsupervised: Horizontal Attacks Meet Simple Classifiers
Abstract
In the last years, Deep Learning algorithms have been browsed and applied to Side-Channel Analysis in order to enhance attack’s performances. In some cases, the proposals came without an indepth analysis allowing to understand the tool, its applicability scenarios, its limitations and the advantages it brings with respect to classical statistical tools. As an example, a study presented at CHES 2021 proposed a corrective iterative framework to perform an unsupervised attack which achieves a 100% key bits recovery. In this paper we analyze the iterative framework and the datasets it was applied onto. The analysis suggests a much easier and interpretable way to both implement such an iterative framework and perform the attack using more conventional solutions, without affecting the attack’s performances.
Metadata
- Available format(s)
PDF- Category
- Attacks and cryptanalysis
- Publication info
- Preprint.
- History
- 2025-06-16: last updated
- 2025-06-16: published
- Short URL
- https://ia.cr/2025/1135
- License
- https://creativecommons.org/licenses/by/4.0/
BibTeX
@misc{cryptoeprint:2025/1135,
author = {Sana Boussam and Ninon Calleja Albillos},
title = {Keep It Unsupervised: Horizontal Attacks Meet Simple Classifiers},
howpublished = {Cryptology {ePrint} Archive, Paper 2025/1135},
year = {2025},
url = {https://eprint.iacr.org/2025/1135}
}