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| Paper Abstract and Keywords | ||
| Presentation | 2022-03-08 10:20 Evaluation of leakage-based LR-PUF's resistance to machine learning attacks Tomoaki Oikawa, Kimiyoshi Usami (SIT)  VLD2021-93 HWS2021-70 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | One of the LSI individual identification technologies is PUF (Physically Unclonable Function), which utilizes the physical characteristics of semiconductors. This technology is expected to make it possible to authenticate genuine products and prevent the distribution of counterfeit products. In recent years, however, the development of machine learning has pointed out the problem of impersonating a genuine product by predicting the response during authentication with high accuracy. In this study, we evaluate the resistance to machine learning attacks using support vector machines and deep neural networks against LR-PUF (Leak Racing PUF), which we previously proposed to improve the resistance to machine learning attacks. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | PUF / Security / Leakage Current / Manufacturing Variation / Machine Learning / Neural Network / / | |
| Reference Info. | IEICE Tech. Rep., vol. 121, no. 412, VLD2021-93, pp. 93-98, March 2022. | |
| Paper # | VLD2021-93 | |
| Date of Issue | 2022-02-28 (VLD, 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 | VLD2021-93 HWS2021-70 |
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| Conference Information | |
| Committee | VLD HWS |
| Conference Date | 2022-03-07 - 2022-03-08 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Online |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Design Technology for System-on-Silicon, Hardware Security, etc. |
| Paper Information | |
| Registration To | VLD |
| Conference Code | 2022-03-VLD-HWS |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Evaluation of leakage-based LR-PUF's resistance to machine learning attacks |
| Sub Title (in English) | |
| Keyword(1) | PUF |
| Keyword(2) | Security |
| Keyword(3) | Leakage Current |
| Keyword(4) | Manufacturing Variation |
| Keyword(5) | Machine Learning |
| Keyword(6) | Neural Network |
| Keyword(7) | |
| Keyword(8) | |
| 1st Author's Name | Tomoaki Oikawa |
| 1st Author's Affiliation | Shibaura Institute of Technology (SIT) |
| 2nd Author's Name | Kimiyoshi Usami |
| 2nd Author's Affiliation | Shibaura Institute of Technology (SIT) |
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| Speaker | Author-1 |
| Date Time | 2022-03-08 10:20:00 |
| Presentation Time | 25 minutes |
| Registration for | VLD |
| Paper # | VLD2021-93, HWS2021-70 |
| Volume (vol) | vol.121 |
| Number (no) | no.412(VLD), no.413(HWS) |
| Page | pp.93-98 |
| #Pages | 6 |
| Date of Issue | 2022-02-28 (VLD, HWS) |
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