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Paper Abstract and Keywords
Presentation 2017-06-16 11:30
Inferring causal parameters of anomalies detected by autoencoder using sparse optimization
Yasuhiro Ikeda, Keisuke Ishibashi, Yusuke Nakano, Keishiro Watanabe, Ryoichi Kawahara (NTT) IN2017-18
Abstract (in Japanese) (See Japanese page) 
(in English) The anomaly detection algorithm based on an autoencoder has attracted much attention.
An autoencoder is a neural network model used for unsupervised learning
and requires only data in normal time as training data to output abnormality of test data
according to how far they are different from the training data.
The autoencoder therefore seems to be desirable as an anomaly detection algorithm
under the situation that abnormal data cannot be obtained sufficiently.
However, identifying the root cause of the anomalies detected by the autoencoder is difficult
since the causal input parameters of the anomalies are not directly indicated.
In this paper, we propose an algorithm for inferring causal input parameters of an autoencoder for anomalies
by using sparse optimization. We also evaluate the algorithm through simulated data and network benchmark data.
Keyword (in Japanese) (See Japanese page) 
(in English) deep Learning / autoencoder / cause estimation / / / / /  
Reference Info. IEICE Tech. Rep., vol. 117, no. 89, IN2017-18, pp. 61-66, June 2017.
Paper # IN2017-18 
Date of Issue 2017-06-08 (IN) 
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 IN2017-18

Conference Information
Committee IN  
Conference Date 2017-06-15 - 2017-06-16 
Place (in Japanese) (See Japanese page) 
Place (in English) Roudou-Fukushi-Kaikan (Koriyama) 
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To IN 
Conference Code 2017-06-IN 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Inferring causal parameters of anomalies detected by autoencoder using sparse optimization 
Sub Title (in English)  
Keyword(1) deep Learning  
Keyword(2) autoencoder  
Keyword(3) cause estimation  
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1st Author's Name Yasuhiro Ikeda  
1st Author's Affiliation Nippon Telegraph and Telephone Corporation (NTT)
2nd Author's Name Keisuke Ishibashi  
2nd Author's Affiliation Nippon Telegraph and Telephone Corporation (NTT)
3rd Author's Name Yusuke Nakano  
3rd Author's Affiliation Nippon Telegraph and Telephone Corporation (NTT)
4th Author's Name Keishiro Watanabe  
4th Author's Affiliation Nippon Telegraph and Telephone Corporation (NTT)
5th Author's Name Ryoichi Kawahara  
5th Author's Affiliation Nippon Telegraph and Telephone Corporation (NTT)
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Speaker Author-1 
Date Time 2017-06-16 11:30:00 
Presentation Time 25 minutes 
Registration for IN 
Paper # IN2017-18 
Volume (vol) vol.117 
Number (no) no.89 
Page pp.61-66 
#Pages 6 
Date of Issue 2017-06-08 (IN) 

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