Paper 2023/1922
One for All, All for Ascon: Ensemble-based Deep Learning Side-channel Analysis
Abstract
In recent years, deep learning-based side-channel analysis (DLSCA) has become an active research topic within the side-channel analysis community. The well-known challenge of hyperparameter tuning in DLSCA encouraged the community to use methods that reduce the effort required to identify an optimal model. One of the successful methods is ensemble learning. While ensemble methods have demonstrated their effectiveness in DLSCA, particularly with AES-based datasets, their efficacy in analyzing symmetric-key cryptographic primitives with different operational mechanics remains unexplored. Ascon was recently announced as the winner of the NIST lightweight cryptography competition. This will lead to broader use of Ascon and a crucial requirement for thorough side-channel analysis of its implementations. With these two considerations in view, we utilize an ensemble of deep neural networks to attack two implementations of Ascon. Using an ensemble of five multilayer perceptrons or convolutional neural networks, we could find the secret key for the Ascon-protected implementation with less than 3 000 traces. To the best of our knowledge, this is the best currently known result. We can also identify the correct key with less than 100 traces for the unprotected implementation of Ascon, which is on par with the state-of-the-art results.
Metadata
- Available format(s)
-
PDF
- Category
- Attacks and cryptanalysis
- Publication info
- Preprint.
- Keywords
- Side-channel AnalysisDeep LearningEnsembleAscon
- Contact author(s)
- a rezaeezade-1 @ tudelft nl
- History
- 2023-12-18: approved
- 2023-12-16: received
- See all versions
- Short URL
- https://ia.cr/2023/1922
- License
-
CC BY
BibTeX
@misc{cryptoeprint:2023/1922,
author = {Azade Rezaeezade and Abraham Basurto-Becerra and Léo Weissbart and Guilherme Perin},
title = {One for All, All for Ascon: Ensemble-based Deep Learning Side-channel Analysis},
howpublished = {Cryptology {ePrint} Archive, Paper 2023/1922},
year = {2023},
url = {https://eprint.iacr.org/2023/1922}
}