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| Paper Abstract and Keywords | ||
| Presentation | 2020-03-06 10:30 A Consideration on Efficient Anomaly Detection Based on Isolation Forest Tsubasa Ikeda, Shinobu Nagayama, Masato Inagi, Shin'ichi Wakabayashi (HCU)  VLD2019-125 HWS2019-98 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | Isolation Forest is a method to detect anomalies by ensemble learning of binary decision trees. It traverses each decision tree according to the input data, and determines that the data is normal if its path length is longer than a threshold, otherwise it is anomaly. In this method, computation time needed for decision of normal data gets longer than necessary. In this study, we use a multi-valued decision tree based Isolation Forest in which the number of branches at each node in decision trees is more than 2, and try to shorten overall path length. Through experiments, we investigate relations of the number of branches with, path length, discrimination time, total number of branches, and detection accuracy to show the effectiveness of the Isolation Forest using multi-valued decision trees. We also consider that it is possible to know useful attributes for detecting anomaly data by checking the attributes frequently selected at each node of each decision tree when anomaly data is detected. We investigate by experiment and consider attribute analysis of anomaly data. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | Anomaly Detection / Isolation Forest / Multi-Valued Decision Trees / / / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 119, no. 443, VLD2019-125, pp. 179-184, March 2020. | |
| Paper # | VLD2019-125 | |
| Date of Issue | 2020-02-26 (VLD, HWS) | |
| 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 | VLD2019-125 HWS2019-98 |
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| Conference Information | |
| Committee | HWS VLD |
| Conference Date | 2020-03-04 - 2020-03-07 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Okinawa Ken Seinen Kaikan |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Design Technology for System-on-Silicon, Hardware Security, etc. |
| Paper Information | |
| Registration To | VLD |
| Conference Code | 2020-03-HWS-VLD |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | A Consideration on Efficient Anomaly Detection Based on Isolation Forest |
| Sub Title (in English) | |
| Keyword(1) | Anomaly Detection |
| Keyword(2) | Isolation Forest |
| Keyword(3) | Multi-Valued Decision Trees |
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| 1st Author's Name | Tsubasa Ikeda |
| 1st Author's Affiliation | Hiroshima City University (HCU) |
| 2nd Author's Name | Shinobu Nagayama |
| 2nd Author's Affiliation | Hiroshima City University (HCU) |
| 3rd Author's Name | Masato Inagi |
| 3rd Author's Affiliation | Hiroshima City University (HCU) |
| 4th Author's Name | Shin'ichi Wakabayashi |
| 4th Author's Affiliation | Hiroshima City University (HCU) |
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| Speaker | Author-1 |
| Date Time | 2020-03-06 10:30:00 |
| Presentation Time | 25 minutes |
| Registration for | VLD |
| Paper # | VLD2019-125, HWS2019-98 |
| Volume (vol) | vol.119 |
| Number (no) | no.443(VLD), no.444(HWS) |
| Page | pp.179-184 |
| #Pages | 6 |
| Date of Issue | 2020-02-26 (VLD, HWS) |
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