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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
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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 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)  
Keyword(1) Machine learning  
Keyword(2) Malicious URLs detection  
Keyword(3) Embedding  
Keyword(4) 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  
2nd Author's Affiliation 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
Date of Issue 2021-02-25 (IT, ISEC, WBS) 


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