Paper Abstract and Keywords |
Presentation |
2017-12-15 14:10
A study of gini importance-based header feature selection methods for cyber attack detection Yuta Kazato, Yuichi Nakatani, Takeshi Okamoto, Akira Shibata (NTT) IN2017-61 |
Abstract |
(in Japanese) |
(See Japanese page) |
(in English) |
Growing the expansion of network services and IoT devices, the risk of cyber attacks is increased by high-frequency and sophistication. Therefore, network service providers must protect their users and services against the huge attacks. Network header feature based machine learning methods for cyber attack detection are effective from the viewpoint of data payload encryption, but non-optimal feature selection result affect both learning time and accuracy. In this paper, we propose the Gini importance-based header feature selection methods for general machine learning algorithms that can detect cyber attacks with performances and efficiencies. We evaluate our feature selection methods in UNSW-NB15 dataset, a comprehensive dataset for network IDSs. As a result, we show the high-accuracy and average 99% detection rates compared with the previous methods. |
Keyword |
(in Japanese) |
(See Japanese page) |
(in English) |
Cyber attack detection / Feature selection / Gini importance / Machine learning / / / / |
Reference Info. |
IEICE Tech. Rep., vol. 117, no. 353, IN2017-61, pp. 91-96, Dec. 2017. |
Paper # |
IN2017-61 |
Date of Issue |
2017-12-07 (IN) |
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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IN2017-61 |
Conference Information |
Committee |
IA IN |
Conference Date |
2017-12-14 - 2017-12-15 |
Place (in Japanese) |
(See Japanese page) |
Place (in English) |
Hiroshima City Univ. |
Topics (in Japanese) |
(See Japanese page) |
Topics (in English) |
Performance Analysis and Simulation, Robustness, Traffic and Throughput Measurement, Quality of Service (QoS) Control, Congestion Control, Overlay Network/P2P, IPv6, Multicast, Routing, DDoS, etc. |
Paper Information |
Registration To |
IN |
Conference Code |
2017-12-IA-IN |
Language |
Japanese |
Title (in Japanese) |
(See Japanese page) |
Sub Title (in Japanese) |
(See Japanese page) |
Title (in English) |
A study of gini importance-based header feature selection methods for cyber attack detection |
Sub Title (in English) |
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Keyword(1) |
Cyber attack detection |
Keyword(2) |
Feature selection |
Keyword(3) |
Gini importance |
Keyword(4) |
Machine learning |
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1st Author's Name |
Yuta Kazato |
1st Author's Affiliation |
Nippon Telegraph and Telephone Corporation (NTT) |
2nd Author's Name |
Yuichi Nakatani |
2nd Author's Affiliation |
Nippon Telegraph and Telephone Corporation (NTT) |
3rd Author's Name |
Takeshi Okamoto |
3rd Author's Affiliation |
Nippon Telegraph and Telephone Corporation (NTT) |
4th Author's Name |
Akira Shibata |
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Nippon Telegraph and Telephone Corporation (NTT) |
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Speaker |
Author-1 |
Date Time |
2017-12-15 14:10:00 |
Presentation Time |
25 minutes |
Registration for |
IN |
Paper # |
IN2017-61 |
Volume (vol) |
vol.117 |
Number (no) |
no.353 |
Page |
pp.91-96 |
#Pages |
6 |
Date of Issue |
2017-12-07 (IN) |
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