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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)  
Keyword(1) Edge-Cloud Computing  
Keyword(2) Edge-Cloud Computing  
Keyword(3) 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  
4th Author's Affiliation 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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