Paper 2023/1922

One for All, All for Ascon: Ensemble-based Deep Learning Side-channel Analysis

Azade Rezaeezade, Delft University of Technology
Abraham Basurto-Becerra, Radboud University
Léo Weissbart, Radboud University
Guilherme Perin, Leiden University
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
Creative Commons Attribution
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}
}
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