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| 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 |
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| 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. |
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| 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 | 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 | SWIM2021-20 SC2021-18 |
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| 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 |
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| Keyword(8) | |
| 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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| Speaker | Author-1 |
| Date Time | 2021-08-27 13:35:00 |
| Presentation Time | 25 minutes |
| Registration for | SC |
| Paper # | SWIM2021-20, SC2021-18 |
| Volume (vol) | vol.121 |
| Number (no) | no.156(SWIM), no.157(SC) |
| Page | pp.46-51 |
| #Pages | 6 |
| Date of Issue | 2021-08-20 (SWIM, SC) |
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