Paper Abstract and Keywords |
Presentation |
2018-07-12 10:55
[Poster Presentation]
A Study on Machine Learning Based Anomaly Detection for a Large Network with Cooperative Edge-Cloud Computing Kengo Tajiri, Yasuhiro Ikeda (NTT), Ryoichi Kawahara (Toyo Univ.), Ryoichi Shinkuma (Kyoto Univ.) RCC2018-49 NS2018-62 RCS2018-107 SR2018-46 ASN2018-43 |
Abstract |
(in Japanese) |
(See Japanese page) |
(in English) |
Early or predictive detection of network failures with anomaly detection techniques is important for providing stable service and reducing operators load. In particular, machine learning-based anomaly detection techniques such as principal component analysis and autoencoder, which learn relationship among cross-domain data in normal states have been attracting attention. However, real-time machine learning-based anomaly detection for a large network is difficult due to transferring and computing delay with enormous log data monitored from a large network. For realization of real-time anomaly detection system, we proposed the anomaly detection techniques with cooperative edge-cloud computing to optimize the transferring and computing duration with the log data monitored from a network and accuracy of the anomaly detection. |
Keyword |
(in Japanese) |
(See Japanese page) |
(in English) |
Edge-Cloud Computing / Edge-Cloud Computing / Distributed Computing / / / / / |
Reference Info. |
IEICE Tech. Rep., vol. 118, no. 126, SR2018-46, pp. 119-120, July 2018. |
Paper # |
SR2018-46 |
Date of Issue |
2018-07-04 (RCC, NS, RCS, SR, ASN) |
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 |
RCC2018-49 NS2018-62 RCS2018-107 SR2018-46 ASN2018-43 |
Conference Information |
Committee |
ASN NS RCS SR RCC |
Conference Date |
2018-07-11 - 2018-07-13 |
Place (in Japanese) |
(See Japanese page) |
Place (in English) |
Hakodate Arena |
Topics (in Japanese) |
(See Japanese page) |
Topics (in English) |
Wireless Distributed Network, Machine Learning and AI for Wireless Communications and Networks, M2M (Machine-to-Machine), D2D (Device-to-Device), IoT(Internet of Things), etc. |
Paper Information |
Registration To |
SR |
Conference Code |
2018-07-ASN-NS-RCS-SR-RCC |
Language |
Japanese |
Title (in Japanese) |
(See Japanese page) |
Sub Title (in Japanese) |
(See Japanese page) |
Title (in English) |
A Study on Machine Learning Based Anomaly Detection for a Large Network with Cooperative Edge-Cloud Computing |
Sub Title (in English) |
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Edge-Cloud Computing |
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Edge-Cloud Computing |
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Distributed Computing |
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1st Author's Name |
Kengo Tajiri |
1st Author's Affiliation |
Nippon Telegraph and Telephone Corporation (NTT) |
2nd Author's Name |
Yasuhiro Ikeda |
2nd Author's Affiliation |
Nippon Telegraph and Telephone Corporation (NTT) |
3rd Author's Name |
Ryoichi Kawahara |
3rd Author's Affiliation |
Toyo University (Toyo Univ.) |
4th Author's Name |
Ryoichi Shinkuma |
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Kyoto University (Kyoto Univ.) |
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Speaker |
Author-1 |
Date Time |
2018-07-12 10:55:00 |
Presentation Time |
80 minutes |
Registration for |
SR |
Paper # |
RCC2018-49, NS2018-62, RCS2018-107, SR2018-46, ASN2018-43 |
Volume (vol) |
vol.118 |
Number (no) |
no.123(RCC), no.124(NS), no.125(RCS), no.126(SR), no.127(ASN) |
Page |
pp.109-110(RCC), pp.115-116(NS), pp.127-128(RCS), pp.119-120(SR), pp.125-126(ASN) |
#Pages |
2 |
Date of Issue |
2018-07-04 (RCC, NS, RCS, SR, ASN) |
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