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| 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 |
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| 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 | |
| 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) | |
| Download PDF | ICSS2017-74 |
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| 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 |
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| 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 |
| #Pages | 6 |
| Date of Issue | 2018-02-28 (ICSS) |
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