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Paper Abstract and Keywords
Presentation 2021-07-16 10:25
Countermeasures against Adversarial Examples using Majority Decision Discriminators for Deep learning-Based Phishing Detection Methods
Yuji Ogawa, Tomotaka Kimura, Jun Cheng (Doshisha Univ.) CS2021-33
Abstract (in Japanese) (See Japanese page) 
(in English) In recent years, the number of phishing attacks has been increasing, and the detection of phishing URLs using deep learning has been attracting attention as a countermeasure. Although the phishing URL detection method using deep learning has a high detection rate, deep learning is vulnerable to AE (Adversarial Examples) techniques that intentionally cause false identifications. Therefore, as a countermeasure against AE techniques, this paper proposes a majority decision discriminator that uses multiple discriminators to determine whether a URL is phishing or normal. We apply one of AE techniques, a one-pixel attack to the proposed method and show that the majority decision method using multiple discriminators is robust
Keyword (in Japanese) (See Japanese page) 
(in English) Phishing Detection / Deep Learning / Adversarial Examples / Majority Decision / / / /  
Reference Info. IEICE Tech. Rep., vol. 121, no. 113, CS2021-33, pp. 78-79, July 2021.
Paper # CS2021-33 
Date of Issue 2021-07-08 (CS) 
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)
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Conference Information
Committee CS  
Conference Date 2021-07-15 - 2021-07-16 
Place (in Japanese) (See Japanese page) 
Place (in English) Online 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Next Generation Networks, Access Networks, Broadband Access, Power Line Communications, Wireless Communication Systems, Coding Systems, etc. 
Paper Information
Registration To CS 
Conference Code 2021-07-CS 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Countermeasures against Adversarial Examples using Majority Decision Discriminators for Deep learning-Based Phishing Detection Methods 
Sub Title (in English)  
Keyword(1) Phishing Detection  
Keyword(2) Deep Learning  
Keyword(3) Adversarial Examples  
Keyword(4) Majority Decision  
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1st Author's Name Yuji Ogawa  
1st Author's Affiliation Doshisha University (Doshisha Univ.)
2nd Author's Name Tomotaka Kimura  
2nd Author's Affiliation Doshisha University (Doshisha Univ.)
3rd Author's Name Jun Cheng  
3rd Author's Affiliation Doshisha University (Doshisha Univ.)
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Speaker Author-1 
Date Time 2021-07-16 10:25:00 
Presentation Time 10 minutes 
Registration for CS 
Paper # CS2021-33 
Volume (vol) vol.121 
Number (no) no.113 
Page pp.78-79 
#Pages
Date of Issue 2021-07-08 (CS) 


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