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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CS2021-33 |
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 |
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Phishing Detection |
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Deep Learning |
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Adversarial Examples |
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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 |
2 |
Date of Issue |
2021-07-08 (CS) |
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