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Paper Abstract and Keywords
Presentation 2021-08-27 13:35
An anomaly detection method to reduce the effect of concept drift
Jaiswal Satish Kumar, Masuda Mineyoshi (Hitachi) SWIM2021-20 SC2021-18
Abstract (in Japanese) (See Japanese page) 
(in English) There is an increasing trend to use machine learning models for monitoring anomalous behavior of CPU, memory,
network, and disk. However, they fail to detect anomalies immediately after abrupt concept drifts unless re-trained with
post drift data. It may take from days to weeks to collect post drift data, interrupting accurate monitoring. We identify that
it is possible to avoid re-training if the concept drifts were caused by events such as change in number of CPU, memory
size, unscheduled deletion of log files, transfer of files, etc. Since these events are expected to become more frequent due
to adoption of container based microservice architecture, it is important to enable accurate anomaly detection immediately
after these events. Our proposed method includes two major steps. First, we confirm that an abrupt concept drift is due to
above-mentioned events. Second, we find a transformation function which can undo the change in data distribution caused by
these events. This allows us to use the same model even after the concept drift. We evaluated our method against well-known
adaptive methods such as Adaptive Random Forest. We found that Adaptive Random Forest takes 2 weeks to adapt while the
proposed method can adapt immediately.
Keyword (in Japanese) (See Japanese page) 
(in English) AIOps / Anomaly Detection / Concept drift / Concept drift adaptation / Linear transformation / / /  
Reference Info. IEICE Tech. Rep., vol. 121, no. 157, SC2021-18, pp. 46-51, Aug. 2021.
Paper # SC2021-18 
Date of Issue 2021-08-20 (SWIM, SC) 
ISSN Print edition: ISSN 0913-5685  Online edition: ISSN 2432-6380
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 SWIM2021-20 SC2021-18

Conference Information
Committee SWIM SC  
Conference Date 2021-08-27 - 2021-08-27 
Place (in Japanese) (See Japanese page) 
Place (in English) Online 
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To SC 
Conference Code 2021-08-SWIM-SC 
Language English (Japanese title is available) 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) An anomaly detection method to reduce the effect of concept drift 
Sub Title (in English)  
Keyword(1) AIOps  
Keyword(2) Anomaly Detection  
Keyword(3) Concept drift  
Keyword(4) Concept drift adaptation  
Keyword(5) Linear transformation  
1st Author's Name Jaiswal Satish Kumar  
1st Author's Affiliation Hitachi, Ltd. Research & Development Group (Hitachi)
2nd Author's Name Masuda Mineyoshi  
2nd Author's Affiliation Hitachi, Ltd. Research & Development Group (Hitachi)
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Date Time 2021-08-27 13:35:00 
Presentation Time 25 
Registration for SC 
Paper # IEICE-SWIM2021-20,IEICE-SC2021-18 
Volume (vol) IEICE-121 
Number (no) no.156(SWIM), no.157(SC) 
Page pp.46-51 
#Pages IEICE-6 
Date of Issue IEICE-SWIM-2021-08-20,IEICE-SC-2021-08-20 

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