Paper Abstract and Keywords |
Presentation |
2018-01-23 11:15
Retraining anomaly detection model using Autoencoder Yasuhiro Ikeda, Keisuke Ishibashi, Yusuke Nakano, Keishiro Watanabe, Ryoichi Kawahara (NTT) IN2017-84 |
Abstract |
(in Japanese) |
(See Japanese page) |
(in English) |
An autoencoder has been attracting much attention as an anomaly detection algorithm.
The autoencoder enables unsupervised learning by using input data as output labels,
and therefore by training the autoencoder with data in normal time,
it is trained to output abnormality of test data
according to how far they are different from the training data.
The autoencoder therefore seems to be desireble as an anomaly detection algorithm
under the situation that abnormal data cannot be obtained sufficiently.
However, since the ``normal state'' of systems will not be static
and false positives due to insufficient training may be unavoidable,
retraining the model according to the data trend and the false-positive detection
is required for continually using the autoencoder for anomaly detection.
In this paper, we propose a retraining algorithm of the autoencoder
including the retraining of ``normal outlier'' which is often a problem in system surveillance
due to temporal high load, for example,
and also evaluate the algorithm through network benchmark data. |
Keyword |
(in Japanese) |
(See Japanese page) |
(in English) |
Deep Learning / Autoencoder / Retraining / / / / / |
Reference Info. |
IEICE Tech. Rep., vol. 117, no. 397, IN2017-84, pp. 77-82, Jan. 2018. |
Paper # |
IN2017-84 |
Date of Issue |
2018-01-15 (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) |
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IN2017-84 |
Conference Information |
Committee |
IN |
Conference Date |
2018-01-22 - 2018-01-23 |
Place (in Japanese) |
(See Japanese page) |
Place (in English) |
WINC AICHI |
Topics (in Japanese) |
(See Japanese page) |
Topics (in English) |
Contents Distribution, Social Networking Services, Data Analytics and Processing Platform, Big data, etc. |
Paper Information |
Registration To |
IN |
Conference Code |
2018-01-IN |
Language |
Japanese |
Title (in Japanese) |
(See Japanese page) |
Sub Title (in Japanese) |
(See Japanese page) |
Title (in English) |
Retraining anomaly detection model using Autoencoder |
Sub Title (in English) |
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Keyword(1) |
Deep Learning |
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Autoencoder |
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Retraining |
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1st Author's Name |
Yasuhiro Ikeda |
1st Author's Affiliation |
NTT (NTT) |
2nd Author's Name |
Keisuke Ishibashi |
2nd Author's Affiliation |
NTT (NTT) |
3rd Author's Name |
Yusuke Nakano |
3rd Author's Affiliation |
NTT (NTT) |
4th Author's Name |
Keishiro Watanabe |
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NTT (NTT) |
5th Author's Name |
Ryoichi Kawahara |
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NTT (NTT) |
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Speaker |
Author-1 |
Date Time |
2018-01-23 11:15:00 |
Presentation Time |
25 minutes |
Registration for |
IN |
Paper # |
IN2017-84 |
Volume (vol) |
vol.117 |
Number (no) |
no.397 |
Page |
pp.77-82 |
#Pages |
6 |
Date of Issue |
2018-01-15 (IN) |
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