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Paper Abstract and Keywords
Presentation 2023-09-08 09:30
Network anomaly detection and failure scale estimation method
Naoya Ogawa, Ryoichi Kawahara (Toyo Univ.) NS2023-57
Abstract (in Japanese) (See Japanese page) 
(in English) In this paper, we propose a network anomaly detection and failure scale estimation method using AI. For anomaly detection, we use an autoencoder that is unsupervised learning, and apply the autoencoder to each communication group for the network to be monitored. By learning the relationship between monitoring items under normal communication conditions, we detect an anomaly from the mean square error obtained from each autoencoder when an anomaly occurs and estimate the scale of the failure from the number of autoencoders determined to be anomalous. We conduct experiments on communication on a virtual network constructed by Mininet, a network emulator, and evaluate the effectiveness of the proposed method.
Keyword (in Japanese) (See Japanese page) 
(in English) Anomaly detection / Failure scale / Autoencoder / Mininet / / / /  
Reference Info. IEICE Tech. Rep., vol. 123, no. 177, NS2023-57, pp. 32-37, Sept. 2023.
Paper # NS2023-57 
Date of Issue 2023-08-31 (NS) 
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 NS2023-57

Conference Information
Committee NS IN CS NV  
Conference Date 2023-09-07 - 2023-09-08 
Place (in Japanese) (See Japanese page) 
Place (in English) Tohoku University 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Session management (SIP/IMS), Interoperability/Standardization, NGN/NwGN/Future networks, Cloud/Data center networks, SDN (OpenFlow, etc.)/NFV, IPv6, Machine learning, etc. 
Paper Information
Registration To NS 
Conference Code 2023-09-NS-IN-CS-NV 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Network anomaly detection and failure scale estimation method 
Sub Title (in English)  
Keyword(1) Anomaly detection  
Keyword(2) Failure scale  
Keyword(3) Autoencoder  
Keyword(4) Mininet  
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1st Author's Name Naoya Ogawa  
1st Author's Affiliation Toyo University (Toyo Univ.)
2nd Author's Name Ryoichi Kawahara  
2nd Author's Affiliation Toyo University (Toyo Univ.)
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Speaker Author-1 
Date Time 2023-09-08 09:30:00 
Presentation Time 25 minutes 
Registration for NS 
Paper # NS2023-57 
Volume (vol) vol.123 
Number (no) no.177 
Page pp.32-37 
#Pages 6 
Date of Issue 2023-08-31 (NS) 

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