Paper 2024/1570

Can KANs Do It? Toward Interpretable Deep Learning-based Side-channel Analysis

Kota Yoshida, Ritsumeikan University
Sengim Karayalcin, Leiden University
Stjepan Picek, Radboud University Nijmegen
Abstract

Recently, deep learning-based side-channel analysis (DLSCA) has emerged as a serious threat against cryptographic implementations. These methods can efficiently break implementations protected with various countermeasures while needing limited manual intervention. To effectively protect implementation, it is therefore crucial to be able to interpret \textbf{how} these models are defeating countermeasures. Several works have attempted to gain a better understanding of the mechanics of these models. However, a fine-grained description remains elusive. To help tackle this challenge, we propose using Kolmogorov-Arnold Networks (KANs). These neural networks were recently introduced and showed competitive performance to multilayer perceptrons (MLPs) on small-scale tasks while being easier to interpret. In this work, we show that KANs are well suited to SCA, performing similarly to MLPs across both simulated and real-world traces. Furthermore, we find specific strategies that the trained models learn for combining mask shares and are able to measure what points in the trace are relevant.

Metadata
Available format(s)
PDF
Category
Attacks and cryptanalysis
Publication info
Preprint.
Keywords
Side-channel AnalysisKolmogorov-Arnold NetworksDeep-LearningInterpretability
Contact author(s)
y0sh1d4 @ fc ritsumei ac jp
s karayalcin @ liacs leidenuniv nl
stjepan picek @ ru nl
History
2024-10-08: approved
2024-10-05: received
See all versions
Short URL
https://ia.cr/2024/1570
License
Creative Commons Attribution
CC BY

BibTeX

@misc{cryptoeprint:2024/1570,
      author = {Kota Yoshida and Sengim Karayalcin and Stjepan Picek},
      title = {Can {KANs} Do It? Toward Interpretable Deep Learning-based Side-channel Analysis},
      howpublished = {Cryptology {ePrint} Archive, Paper 2024/1570},
      year = {2024},
      url = {https://eprint.iacr.org/2024/1570}
}
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