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Paper Abstract and Keywords
Presentation 2016-11-25 16:10
Performance of link-mining techniques to detect malicious websites
Yasuhiro Takano, Daiki Ito, Tatsuya Nagai (Kobe Univ.), Masaki Kamizono (PwC Cyber Services), Masami Mohri (Gifu Univ.), Yoshiaki Shiraishi (Kobe Univ.), Yuji Hoshizawa (PwC Cyber Services), Masakatu Morii (Kobe Univ.) ICSS2016-44
Abstract (in Japanese) (See Japanese page) 
(in English) Conventional techniques to avoid malicious websites techniques by referring URL's keywords reported in black lists have been studied. Since attackers can modify the URL quite often, however, the conventional techniques are concerned that they are difficult to follow the frequent updates. Our previous contribution has shown that the malicious websites have a certain correlation among them. This paper evaluates, therefore, performance of supervised-inkmining techniques to detect the malicious websites by inputting the link structure captured from the actual websites. The experimental evaluation results shows that by determining the networks automatically the convolutional neural networks (CNN) algorithms achieves the accuracy = 87%, which outperform the support vector classification (SVC) techniques significantly.
Keyword (in Japanese) (See Japanese page) 
(in English) drive-by-download attack / linkmining / support vector classification (SVC) / convolutional neural networks (CNN) / / / /  
Reference Info. IEICE Tech. Rep., vol. 116, no. 328, ICSS2016-44, pp. 31-35, Nov. 2016.
Paper # ICSS2016-44 
Date of Issue 2016-11-18 (ICSS) 
ISSN Print edition: ISSN 0913-5685    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 ICSS  
Conference Date 2016-11-25 - 2016-11-25 
Place (in Japanese) (See Japanese page) 
Place (in English) Institute of Information Security 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Information and Communication System Security, etc. 
Paper Information
Registration To ICSS 
Conference Code 2016-11-ICSS 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Performance of link-mining techniques to detect malicious websites 
Sub Title (in English)  
Keyword(1) drive-by-download attack  
Keyword(2) linkmining  
Keyword(3) support vector classification (SVC)  
Keyword(4) convolutional neural networks (CNN)  
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1st Author's Name Yasuhiro Takano  
1st Author's Affiliation Kobe University (Kobe Univ.)
2nd Author's Name Daiki Ito  
2nd Author's Affiliation Kobe University (Kobe Univ.)
3rd Author's Name Tatsuya Nagai  
3rd Author's Affiliation Kobe University (Kobe Univ.)
4th Author's Name Masaki Kamizono  
4th Author's Affiliation PwC Cyber Services LLC (PwC Cyber Services)
5th Author's Name Masami Mohri  
5th Author's Affiliation Gifu University (Gifu Univ.)
6th Author's Name Yoshiaki Shiraishi  
6th Author's Affiliation Kobe University (Kobe Univ.)
7th Author's Name Yuji Hoshizawa  
7th Author's Affiliation PwC Cyber Services LLC (PwC Cyber Services)
8th Author's Name Masakatu Morii  
8th Author's Affiliation Kobe University (Kobe Univ.)
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Speaker Author-1 
Date Time 2016-11-25 16:10:00 
Presentation Time 25 minutes 
Registration for ICSS 
Paper # ICSS2016-44 
Volume (vol) vol.116 
Number (no) no.328 
Page pp.31-35 
#Pages 5 
Date of Issue 2016-11-18 (ICSS) 

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