Paper Abstract and Keywords |
Presentation |
2019-03-05 11:30
A study on Maliciousness Measurement in Cyber Threat Intelligence Using Graph Convolutional Networks Yuta Kazato, Yoshihide Nakagawa, Yuichi Nakatani (NTT) IN2018-128 |
Abstract |
(in Japanese) |
(See Japanese page) |
(in English) |
Cyber threat information (CTI) sharing is one of the important functions to protect end-users and services from cyber-attacks, and is growing in popularity all over the world.
However, it is necessary for security operators and analysts to analyze CTI and estimate maliciousness scores of domains, URLs and IPs, which are important to determine which CTI to use. Previous studies have focused on the structural or graph-based features of CTI using machine learning methods to measure maliciousness scores.
In this paper, we propose Graph Convolutional Networks (GCN) to measure maliciousness scores. GCN can deal with both structural and graph-based features in a deep learning scheme. We construct graphs of CTI using OSINT information (DNS and RDAP), evaluate the accuracy of GCN, and find some technical problems. |
Keyword |
(in Japanese) |
(See Japanese page) |
(in English) |
Cyber Threat Intelligence / Graph Convolutional Networks / Malicious Measurement / CTI / GCN / / / |
Reference Info. |
IEICE Tech. Rep., vol. 118, no. 466, IN2018-128, pp. 265-270, March 2019. |
Paper # |
IN2018-128 |
Date of Issue |
2019-02-25 (IN) |
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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IN2018-128 |
Conference Information |
Committee |
IN NS |
Conference Date |
2019-03-04 - 2019-03-05 |
Place (in Japanese) |
(See Japanese page) |
Place (in English) |
Okinawa Convention Center |
Topics (in Japanese) |
(See Japanese page) |
Topics (in English) |
General |
Paper Information |
Registration To |
IN |
Conference Code |
2019-03-IN-NS |
Language |
Japanese |
Title (in Japanese) |
(See Japanese page) |
Sub Title (in Japanese) |
(See Japanese page) |
Title (in English) |
A study on Maliciousness Measurement in Cyber Threat Intelligence Using Graph Convolutional Networks |
Sub Title (in English) |
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Cyber Threat Intelligence |
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Graph Convolutional Networks |
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Malicious Measurement |
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CTI |
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GCN |
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1st Author's Name |
Yuta Kazato |
1st Author's Affiliation |
NTT Corporation (NTT) |
2nd Author's Name |
Yoshihide Nakagawa |
2nd Author's Affiliation |
NTT Corporation (NTT) |
3rd Author's Name |
Yuichi Nakatani |
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NTT Corporation (NTT) |
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Speaker |
Author-1 |
Date Time |
2019-03-05 11:30:00 |
Presentation Time |
20 minutes |
Registration for |
IN |
Paper # |
IN2018-128 |
Volume (vol) |
vol.118 |
Number (no) |
no.466 |
Page |
pp.265-270 |
#Pages |
6 |
Date of Issue |
2019-02-25 (IN) |
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