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| Presentation | 2021-04-12 14:40 On the Performance Evaluation of Deep-Learning Based Side-Channel Attacks Akira Ito, Rei Ueno, Naofumi Homma (Tohoku Univ.)  HWS2021-8 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | This paper presents a method for estimating the lower bound of success rate (SR) and the upper bound of guessing entropy (GE) on deep-learning-based side-channel attacks (DL-SCAs) for the purpose of direct and quantitative performance evaluation. In conventional side-channel attacks, SR and GE are widely used as indicators for measuring the efficiency of attacks. On the other hand, in DL-SCA, it is pointed out that performance evaluation metrics generally used in machine learning such as Accuracy and Precision are not effective in estimating SR and GE. In this paper, we consider that the negative log-likelihood used in DL-SCA can be reduced to the sum of independent random variables, and derive a tighter GE upper bound and SR lower bound using a probability concentration inequality. Through attack experiments on different data sets, we confirm the effectiveness of the upper bound of GE and the lower bound of SR derived by the proposed method. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | Side-channel attacks / Deep learning / Probability concentration inequalities / / / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 121, no. 1, HWS2021-8, pp. 33-38, April 2021. | |
| Paper # | HWS2021-8 | |
| Date of Issue | 2021-04-05 (HWS) | |
| 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 | HWS2021-8 |
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| Conference Information | |
| Committee | HWS |
| Conference Date | 2021-04-12 - 2021-04-12 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Tokyo University/Online |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Hardware Security |
| Paper Information | |
| Registration To | HWS |
| Conference Code | 2021-04-HWS |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | On the Performance Evaluation of Deep-Learning Based Side-Channel Attacks |
| Sub Title (in English) | |
| Keyword(1) | Side-channel attacks |
| Keyword(2) | Deep learning |
| Keyword(3) | Probability concentration inequalities |
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| Keyword(8) | |
| 1st Author's Name | Akira Ito |
| 1st Author's Affiliation | Tohoku University (Tohoku Univ.) |
| 2nd Author's Name | Rei Ueno |
| 2nd Author's Affiliation | Tohoku University (Tohoku Univ.) |
| 3rd Author's Name | Naofumi Homma |
| 3rd Author's Affiliation | Tohoku University (Tohoku Univ.) |
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| Speaker | Author-1 |
| Date Time | 2021-04-12 14:40:00 |
| Presentation Time | 25 minutes |
| Registration for | HWS |
| Paper # | HWS2021-8 |
| Volume (vol) | vol.121 |
| Number (no) | no.1 |
| Page | pp.33-38 |
| #Pages | 6 |
| Date of Issue | 2021-04-05 (HWS) |
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