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Paper Abstract and Keywords
Presentation 2023-07-24 15:00
Tolerance Evaluation Cost Reduction of Deep-Learning-Based Side-Channel Attack Using Signal-to-Noise Ratio of Leakage Traces
Tatsuya Sakagami, Masaki Himuro, Kengo Iokibe, Yoshitaka Toyota (Okayama Univ.) ISEC2023-17 SITE2023-11 BioX2023-20 HWS2023-17 ICSS2023-14 EMM2023-17
Abstract (in Japanese) (See Japanese page) 
(in English) Side-channel attacks (SCA) have been proposed to decrypt modern cryptography by analyzing the physical behavior of cryptographic circuits, and have become a threat to information leakage. SCAs can also break cryptography with masking countermeasures, a popular countermeasure to SCAs, by using deep learning. Therefore, it is necessary to evaluate SCA tolerance for each of possible countermeasures when designing an electronic device with cryptographic functions. For evaluating resistance to DL-SCA, carrying out DL-SCAs on plenty of side-channel leakage traces is required. To reduce the cost of DL-SCA resistance evaluation, this paper proposes a method to reduce the number of measurements of leakage traces for evaluation by simulating the leakage traces after SCA countermeasures are implemented. In the proposed method, leakage traces are measured under a certain evaluation condition. Leakage traces for other evaluation conditions, which demonstrate conditions in DL-SCA countermeasures are implemented, are calculated from the measured traces by superimposing noise components for those other conditions. This study predicts the DL-SCA tolerance based on the proposed method. We measured leakage traces of a microcontroller implementing an AES algorithm with the masking countermeasure at multiple leakage locations. We performed CPA to create noise waveforms and then constructed simulated traces for one of the leakage locations. The results of the DL-SCA showed that 6 out of 12 secret key bytes were recovered from the simulated leakage traces. This is almost consistent with the measured one, in which four bytes were revealed.
Keyword (in Japanese) (See Japanese page) 
(in English) Deep Learning SCA / Resistance evaluation / AES / Micirocontroller / Correlation Power Analysis / / /  
Reference Info. IEICE Tech. Rep., vol. 123, no. 132, HWS2023-17, pp. 19-24, July 2023.
Paper # HWS2023-17 
Date of Issue 2023-07-17 (ISEC, SITE, BioX, HWS, ICSS, EMM) 
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 ISEC2023-17 SITE2023-11 BioX2023-20 HWS2023-17 ICSS2023-14 EMM2023-17

Conference Information
Committee EMM BioX ISEC SITE ICSS HWS IPSJ-CSEC IPSJ-SPT 
Conference Date 2023-07-24 - 2023-07-25 
Place (in Japanese) (See Japanese page) 
Place (in English) Hokkaido Jichiro Kaikan 
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To HWS 
Conference Code 2023-07-EMM-BioX-ISEC-SITE-ICSS-HWS-CSEC-SPT 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Tolerance Evaluation Cost Reduction of Deep-Learning-Based Side-Channel Attack Using Signal-to-Noise Ratio of Leakage Traces 
Sub Title (in English)  
Keyword(1) Deep Learning SCA  
Keyword(2) Resistance evaluation  
Keyword(3) AES  
Keyword(4) Micirocontroller  
Keyword(5) Correlation Power Analysis  
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1st Author's Name Tatsuya Sakagami  
1st Author's Affiliation Okayama University (Okayama Univ.)
2nd Author's Name Masaki Himuro  
2nd Author's Affiliation Okayama University (Okayama Univ.)
3rd Author's Name Kengo Iokibe  
3rd Author's Affiliation Okayama University (Okayama Univ.)
4th Author's Name Yoshitaka Toyota  
4th Author's Affiliation Okayama University (Okayama Univ.)
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Speaker Author-1 
Date Time 2023-07-24 15:00:00 
Presentation Time 20 minutes 
Registration for HWS 
Paper # ISEC2023-17, SITE2023-11, BioX2023-20, HWS2023-17, ICSS2023-14, EMM2023-17 
Volume (vol) vol.123 
Number (no) no.129(ISEC), no.130(SITE), no.131(BioX), no.132(HWS), no.133(ICSS), no.134(EMM) 
Page pp.19-24 
#Pages 6 
Date of Issue 2023-07-17 (ISEC, SITE, BioX, HWS, ICSS, EMM) 

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