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Paper Abstract and Keywords
Presentation 2019-03-01 15:45
On Machine Learning Attack Tolerance for PUF-based Device Authentication System
Tomoki Iizuka (UTokyo), Yasuhiro Ogasahara, Toshihiro Katashita, Yohei Hori (AIST), Hiromitsu Awano (Osaka Univ.), Makoto Ikeda (UTokyo) VLD2018-133 HWS2018-96
Abstract (in Japanese) (See Japanese page) 
(in English) Double-Arbiter PUF (DAPUF) and PL-PUF are known to be highly resistant to machine learning attacks.
In this paper, we proposed a deep neural network-based modeling attack for DAPUF and PL-PUF.
Proposed network successfully predicts unknown responses of DAPUF with probability 21.1% higher than the conventional method. Furthermore, we presented that prediction rate of PL-PUF depends on the response bit to be predicted and number of oscillation cycles.
Based on experimental results, we examined the vulnerability of the authentication systems for machine learning attacks considering the environmental variation.
Keyword (in Japanese) (See Japanese page) 
(in English) PUF / hardware security / deep neural network / machine learning attack / modeling attack / / /  
Reference Info. IEICE Tech. Rep., vol. 118, no. 458, HWS2018-96, pp. 237-242, Feb. 2019.
Paper # HWS2018-96 
Date of Issue 2019-02-20 (VLD, HWS) 
ISSN Print edition: ISSN 0913-5685    Online edition: ISSN 2432-6380
Copyright
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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)
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Conference Information
Committee HWS VLD  
Conference Date 2019-02-27 - 2019-03-02 
Place (in Japanese) (See Japanese page) 
Place (in English) Okinawa Ken Seinen Kaikan 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Design Technology for System-on-Silicon, Hardware Security, etc. 
Paper Information
Registration To HWS 
Conference Code 2019-02-HWS-VLD 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) On Machine Learning Attack Tolerance for PUF-based Device Authentication System 
Sub Title (in English)  
Keyword(1) PUF  
Keyword(2) hardware security  
Keyword(3) deep neural network  
Keyword(4) machine learning attack  
Keyword(5) modeling attack  
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1st Author's Name Tomoki Iizuka  
1st Author's Affiliation The University of Tokyo (UTokyo)
2nd Author's Name Yasuhiro Ogasahara  
2nd Author's Affiliation National Institute of Advanced Industrial Science and Technology (AIST)
3rd Author's Name Toshihiro Katashita  
3rd Author's Affiliation National Institute of Advanced Industrial Science and Technology (AIST)
4th Author's Name Yohei Hori  
4th Author's Affiliation National Institute of Advanced Industrial Science and Technology (AIST)
5th Author's Name Hiromitsu Awano  
5th Author's Affiliation Osaka University (Osaka Univ.)
6th Author's Name Makoto Ikeda  
6th Author's Affiliation The University of Tokyo (UTokyo)
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Speaker Author-1 
Date Time 2019-03-01 15:45:00 
Presentation Time 25 minutes 
Registration for HWS 
Paper # VLD2018-133, HWS2018-96 
Volume (vol) vol.118 
Number (no) no.457(VLD), no.458(HWS) 
Page pp.237-242 
#Pages
Date of Issue 2019-02-20 (VLD, HWS) 


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