Paper Abstract and Keywords |
Presentation |
2021-03-05 14:45
Consideration of embedding methods and machine learning models for detecting malicious URLs Qisheng Chen, Kazumasa omote (Univ. of Tsukuba) IT2020-157 ISEC2020-87 WBS2020-76 |
Abstract |
(in Japanese) |
(See Japanese page) |
(in English) |
Nowadays, Internet access is becoming more and more popular, which makes the harm of malicious websites more and more serious. There are many solutions to solve this problem such as Google Safe Browsing, which check whether the website is malicious. A blacklist method is very useful for detect malicious URL but there are still many shortcomings. For example, a lot of new malicious websites are generated every day, and the speed of blacklist expansion can not keep up. In this paper, we use different embedding methods and different machine learning models to detecting malicious URLs. Besides, we compared the accuracy of these embedding methods and machine learning models. In our evaluation, the embedding algorithm TF-IDF and Token segmentation method obtain a good performance and we draw a conclusion that segmentation method plays an important role in malicious URLs detection. |
Keyword |
(in Japanese) |
(See Japanese page) |
(in English) |
Machine learning / Malicious URLs detection / Embedding / Segmentation method / / / / |
Reference Info. |
IEICE Tech. Rep., vol. 120, no. 411, ISEC2020-87, pp. 281-287, March 2021. |
Paper # |
ISEC2020-87 |
Date of Issue |
2021-02-25 (IT, ISEC, WBS) |
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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IT2020-157 ISEC2020-87 WBS2020-76 |
Conference Information |
Committee |
WBS IT ISEC |
Conference Date |
2021-03-04 - 2021-03-05 |
Place (in Japanese) |
(See Japanese page) |
Place (in English) |
Online |
Topics (in Japanese) |
(See Japanese page) |
Topics (in English) |
Joint Meeting of WBS, IT, and ISEC |
Paper Information |
Registration To |
ISEC |
Conference Code |
2021-03-WBS-IT-ISEC |
Language |
English (Japanese title is available) |
Title (in Japanese) |
(See Japanese page) |
Sub Title (in Japanese) |
(See Japanese page) |
Title (in English) |
Consideration of embedding methods and machine learning models for detecting malicious URLs |
Sub Title (in English) |
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Machine learning |
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Malicious URLs detection |
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Embedding |
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Segmentation method |
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1st Author's Name |
Qisheng Chen |
1st Author's Affiliation |
University of Tsukuba (Univ. of Tsukuba) |
2nd Author's Name |
Kazumasa omote |
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University of Tsukuba (Univ. of Tsukuba) |
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Speaker |
Author-1 |
Date Time |
2021-03-05 14:45:00 |
Presentation Time |
25 minutes |
Registration for |
ISEC |
Paper # |
IT2020-157, ISEC2020-87, WBS2020-76 |
Volume (vol) |
vol.120 |
Number (no) |
no.410(IT), no.411(ISEC), no.412(WBS) |
Page |
pp.281-287 |
#Pages |
7 |
Date of Issue |
2021-02-25 (IT, ISEC, WBS) |
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