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Paper Abstract and Keywords
Presentation 2025-03-08 09:20
Interpretable Deep Learning-based Side-channel Analysis Using Kolmogorov-Arnold Networks
Kota Yoshida (Ritsumeikan Univ.), Sengim Karayalcin (Leiden Univ.), Stjepan Picek (Radboud Univ.) VLD2024-134 HWS2024-105 ICD2024-125
Abstract (in Japanese) (See Japanese page) 
(in English) Deep learning-based side-channel analysis (DL-SCA) achieves superior key-recovery performance to conventional statistic-based methods.
However, the problem of interpretability in deep learning models makes it difficult to feed back the analysis results into cryptographic implementations.
In this study, we explore the use of Kolmogorov-Arnold Networks (KAN) as an alternative to a multi-layer perceptrons (MLP) that are widely used in conventional DL-SCA.
KAN achieves comparable inference accuracy to MLPs and can interpret what the trained model learned by visualizing trained activation functions.
We focused on a 2-share masking scheme, and conducted experiments using both simulations and the experiments with the ASCAD to reveal how KAN exploits leakages from masked intermediate values to predict labels (the LSB of the intermediate value).
Our evaluation results demonstrate that KAN learns the relationship between the sum of the HW leakages from the two shares and the expected occurrence of the LSB.
Keyword (in Japanese) (See Japanese page) 
(in English) DL-SCA / Kolmogorov-Arnold Networks / Side-channel attacks / / / / /  
Reference Info. IEICE Tech. Rep., vol. 124, no. 401, HWS2024-105, pp. 176-181, March 2025.
Paper # HWS2024-105 
Date of Issue 2025-02-26 (VLD, HWS, ICD) 
ISSN Online edition: ISSN 2432-6380
Copyright
and
reproduction		 All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034)
Download PDF VLD2024-134 HWS2024-105 ICD2024-125

Conference Information
Committee HWS ICD VLD  
Conference Date 2025-03-05 - 2025-03-08 
Place (in Japanese) (See Japanese page) 
Place (in English)  
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To HWS 
Conference Code 2025-03-HWS-ICD-VLD 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Interpretable Deep Learning-based Side-channel Analysis Using Kolmogorov-Arnold Networks 
Sub Title (in English)  
Keyword(1) DL-SCA  
Keyword(2) Kolmogorov-Arnold Networks  
Keyword(3) Side-channel attacks  
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1st Author's Name Kota Yoshida  
1st Author's Affiliation Ritsumeikan University (Ritsumeikan Univ.)
2nd Author's Name Sengim Karayalcin  
2nd Author's Affiliation Leiden University (Leiden Univ.)
3rd Author's Name Stjepan Picek  
3rd Author's Affiliation Radboud University (Radboud Univ.)
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Speaker Author-1 
Date Time 2025-03-08 09:20:00 
Presentation Time 25 minutes 
Registration for HWS 
Paper # VLD2024-134, HWS2024-105, ICD2024-125 
Volume (vol) vol.124 
Number (no) no.400(VLD), no.401(HWS), no.402(ICD) 
Page pp.176-181 
#Pages 6 
Date of Issue 2025-02-26 (VLD, HWS, ICD) 

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