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| Presentation | 2016-11-17 14:00 Anomaly Deteciton with K-Means -- Comparison with Supervised Methods -- Hisashi Takahara (UNP)  IBISML2016-75 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | Today, almost all computers have access to the Internet. Computers connected to the Internet are susceptible to various kinds of attacks. Anomaly detection is one form of prevention. Among anomaly detection methods, there are “detection with signature” and “detection with machine learning techniques”. In detection with machine learning techniques, there are two approaches. One is “supervised” and the other “unsupervised”. The former refers to data from behavior of known attacks (training data), so it may be influenced by behavior of known attacks, but it may not detect unknown attacks adequately. The latter does not use training data and is not influenced by known attacks. Consequently, we hypothesized that unsupervised methods would detect unknown attacks better than supervised methods. To check our hypothesis, we compared unsupervised and supervised methods of anomaly detection. We used k-means as the unsupervised method and tested this method with clusters ranging in size from to 2 to 7. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | Machine Learning / K-Means / Supervised Method / Unsupervised Method / Anomaly Detection / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 116, no. 300, IBISML2016-75, pp. 207-214, Nov. 2016. | |
| Paper # | IBISML2016-75 | |
| Date of Issue | 2016-11-09 (IBISML) | |
| 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 | IBISML2016-75 |
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| Conference Information | |
| Committee | IBISML |
| Conference Date | 2016-11-16 - 2016-11-18 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Kyoto Univ. |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Information-Based Induction Science Workshop (IBIS2016) |
| Paper Information | |
| Registration To | IBISML |
| Conference Code | 2016-11-IBISML |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Anomaly Deteciton with K-Means |
| Sub Title (in English) | Comparison with Supervised Methods |
| Keyword(1) | Machine Learning |
| Keyword(2) | K-Means |
| Keyword(3) | Supervised Method |
| Keyword(4) | Unsupervised Method |
| Keyword(5) | Anomaly Detection |
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| 1st Author's Name | Hisashi Takahara |
| 1st Author's Affiliation | University of NIIGATA PREFECTURE (UNP) |
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| Speaker | Author-1 |
| Date Time | 2016-11-17 14:00:00 |
| Presentation Time | 180 minutes |
| Registration for | IBISML |
| Paper # | IBISML2016-75 |
| Volume (vol) | vol.116 |
| Number (no) | no.300 |
| Page | pp.207-214 |
| #Pages | 8 |
| Date of Issue | 2016-11-09 (IBISML) |
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