Paper 2024/1570
Can KANs Do It? Toward Interpretable Deep Learning-based Side-channel Analysis
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)
- 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
-
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} }