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Paper Abstract and Keywords
Presentation 2018-03-08 12:00
Analyzing web application vulnerability reports by machine learning
Takaaki Nagumo, Yuusuke Kubo, Yasuhiro Kobayashi, Wataru Hasegawa, Takahiro Hamada (NTT Com) ICSS2017-74
Abstract (in Japanese) (See Japanese page) 
(in English) Web application vulnerability testing is important for preventing cyberattacks. However, it is difficult to test all URLs and vulnerabilities in a web application, due to its complexity and enormousness of test items. Vulnerability reports of testing tools include false positives, although a tester uses the tools to reduce testing costs for large-scale applications. We propose our analyzing method for web vulnerability testing reports by machine learning in order to reduce the cost of checking false positives.
Keyword (in Japanese) (See Japanese page) 
(in English) Web Application Vulnerability / DAST / Machine Learning / Deep Learning / / / /  
Reference Info. IEICE Tech. Rep., vol. 117, no. 481, ICSS2017-74, pp. 139-144, March 2018.
Paper # ICSS2017-74 
Date of Issue 2018-02-28 (ICSS) 
ISSN Print edition: ISSN 0913-5685    Online edition: ISSN 2432-6380
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)
Download PDF ICSS2017-74

Conference Information
Committee ICSS IPSJ-SPT  
Conference Date 2018-03-07 - 2018-03-08 
Place (in Japanese) (See Japanese page) 
Place (in English) Okinawa Hokubu Koyou Nouryoku Kaihatsu Sougou Center 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Security, Trust, etc. 
Paper Information
Registration To ICSS 
Conference Code 2018-03-ICSS-SPT 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Analyzing web application vulnerability reports by machine learning 
Sub Title (in English)  
Keyword(1) Web Application Vulnerability  
Keyword(2) DAST  
Keyword(3) Machine Learning  
Keyword(4) Deep Learning  
1st Author's Name Takaaki Nagumo  
1st Author's Affiliation NTT Communications Corporation (NTT Com)
2nd Author's Name Yuusuke Kubo  
2nd Author's Affiliation NTT Communications Corporation (NTT Com)
3rd Author's Name Yasuhiro Kobayashi  
3rd Author's Affiliation NTT Communications Corporation (NTT Com)
4th Author's Name Wataru Hasegawa  
4th Author's Affiliation NTT Communications Corporation (NTT Com)
5th Author's Name Takahiro Hamada  
5th Author's Affiliation NTT Communications Corporation (NTT Com)
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Speaker Author-1 
Date Time 2018-03-08 12:00:00 
Presentation Time 25 minutes 
Registration for ICSS 
Paper # ICSS2017-74 
Volume (vol) vol.117 
Number (no) no.481 
Page pp.139-144 
Date of Issue 2018-02-28 (ICSS) 

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