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| Presentation | 2020-11-27 14:00 [Encouragement Talk] A Study of Accuracy Improvement of Silent Anomaly Detection Using Machine Learning in Mobile Access Networks Masatomo Suzuki, Akihiro Shiozu, Yasushi Konou (DOCOMO Technology)  ICM2020-32 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | Mobile communication services have become indispensable in our daily life. While it is important to ensure the stability and reliability of mobile networks, it is important to improve the efficiency of network operations, which is expected to grow due to the diversification of services and the complexity of the network configuration, such as 5G. The authors have been working on silent anomaly detection in base stations using AutoEncoder to solve the problem, and have confirmed certain results so far. In this paper, we describe our efforts to reduce false positives in silent anomaly detection (i.e., AI judges anomalies in spite of normal conditions) and the results of the application. |
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| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | Mobile Access Networks / Anomaly Detection / Machine Learning / / / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 120, no. 259, ICM2020-32, pp. 67-72, Nov. 2020. | |
| Paper # | ICM2020-32 | |
| Date of Issue | 2020-11-19 (ICM) | |
| 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) | |
| Download PDF | ICM2020-32 |
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| Conference Information | |
| Committee | NS ICM CQ NV |
| Conference Date | 2020-11-26 - 2020-11-27 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Online |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Network quality, Network measurement/management, Network virtualization, Network service, Blockchain, Security, Network intelligence, etc. |
| Paper Information | |
| Registration To | ICM |
| Conference Code | 2020-11-NS-ICM-CQ-NV |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | A Study of Accuracy Improvement of Silent Anomaly Detection Using Machine Learning in Mobile Access Networks |
| Sub Title (in English) | |
| Keyword(1) | Mobile Access Networks |
| Keyword(2) | Anomaly Detection |
| Keyword(3) | Machine Learning |
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| 1st Author's Name | Masatomo Suzuki |
| 1st Author's Affiliation | DOCOMO Technology, Inc. (DOCOMO Technology) |
| 2nd Author's Name | Akihiro Shiozu |
| 2nd Author's Affiliation | DOCOMO Technology, Inc. (DOCOMO Technology) |
| 3rd Author's Name | Yasushi Konou |
| 3rd Author's Affiliation | DOCOMO Technology, Inc. (DOCOMO Technology) |
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| Speaker | Author-1 |
| Date Time | 2020-11-27 14:00:00 |
| Presentation Time | 25 minutes |
| Registration for | ICM |
| Paper # | ICM2020-32 |
| Volume (vol) | vol.120 |
| Number (no) | no.259 |
| Page | pp.67-72 |
| #Pages | 6 |
| Date of Issue | 2020-11-19 (ICM) |
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