IEICE TRANSACTIONS on Information
A Novel Anomaly Detection Framework Based on Model Serialization
Summary :
Recently, multivariate time-series data has been generated in various environments, such as sensor networks and IoT, making anomaly detection in time-series data an essential research topic. Unsupervised learning anomaly detectors identify anomalies by training a model on normal data and producing high residuals for abnormal observations. However, a fundamental issue arises as anomalies do not consistently result in high residuals, necessitating a focus on the time-series patterns of residuals rather than individual residual sizes. In this paper, we present a novel framework comprising two serialized anomaly detectors: the first model calculates residuals as usual, while the second one evaluates the time-series pattern of the computed residuals to determine whether they are normal or abnormal. Experiments conducted on real-world time-series data demonstrate the effectiveness of our proposed framework.
- Publication
- IEICE TRANSACTIONS on Information Vol.E107-D No.3 pp.420-423
- Publication Date
- 2024/03/01
- Publicized
- 2023/11/21
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2023EDL8024
- Type of Manuscript
- LETTER
- Category
- Artificial Intelligence, Data Mining
Authors
Byeongtae PARK
Hanyang University
Dong-Kyu CHAE
Hanyang University
Keyword
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Byeongtae PARK, Dong-Kyu CHAE, "A Novel Anomaly Detection Framework Based on Model Serialization" in IEICE TRANSACTIONS on Information,
vol. E107-D, no. 3, pp. 420-423, March 2024, doi: 10.1587/transinf.2023EDL8024.
Abstract: Recently, multivariate time-series data has been generated in various environments, such as sensor networks and IoT, making anomaly detection in time-series data an essential research topic. Unsupervised learning anomaly detectors identify anomalies by training a model on normal data and producing high residuals for abnormal observations. However, a fundamental issue arises as anomalies do not consistently result in high residuals, necessitating a focus on the time-series patterns of residuals rather than individual residual sizes. In this paper, we present a novel framework comprising two serialized anomaly detectors: the first model calculates residuals as usual, while the second one evaluates the time-series pattern of the computed residuals to determine whether they are normal or abnormal. Experiments conducted on real-world time-series data demonstrate the effectiveness of our proposed framework.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2023EDL8024/_p
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@ARTICLE{e107-d_3_420,
author={Byeongtae PARK, Dong-Kyu CHAE, },
journal={IEICE TRANSACTIONS on Information},
title={A Novel Anomaly Detection Framework Based on Model Serialization},
year={2024},
volume={E107-D},
number={3},
pages={420-423},
abstract={Recently, multivariate time-series data has been generated in various environments, such as sensor networks and IoT, making anomaly detection in time-series data an essential research topic. Unsupervised learning anomaly detectors identify anomalies by training a model on normal data and producing high residuals for abnormal observations. However, a fundamental issue arises as anomalies do not consistently result in high residuals, necessitating a focus on the time-series patterns of residuals rather than individual residual sizes. In this paper, we present a novel framework comprising two serialized anomaly detectors: the first model calculates residuals as usual, while the second one evaluates the time-series pattern of the computed residuals to determine whether they are normal or abnormal. Experiments conducted on real-world time-series data demonstrate the effectiveness of our proposed framework.},
keywords={},
doi={10.1587/transinf.2023EDL8024},
ISSN={1745-1361},
month={March},}
Copy
TY - JOUR
TI - A Novel Anomaly Detection Framework Based on Model Serialization
T2 - IEICE TRANSACTIONS on Information
SP - 420
EP - 423
AU - Byeongtae PARK
AU - Dong-Kyu CHAE
PY - 2024
DO - 10.1587/transinf.2023EDL8024
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E107-D
IS - 3
JA - IEICE TRANSACTIONS on Information
Y1 - March 2024
AB - Recently, multivariate time-series data has been generated in various environments, such as sensor networks and IoT, making anomaly detection in time-series data an essential research topic. Unsupervised learning anomaly detectors identify anomalies by training a model on normal data and producing high residuals for abnormal observations. However, a fundamental issue arises as anomalies do not consistently result in high residuals, necessitating a focus on the time-series patterns of residuals rather than individual residual sizes. In this paper, we present a novel framework comprising two serialized anomaly detectors: the first model calculates residuals as usual, while the second one evaluates the time-series pattern of the computed residuals to determine whether they are normal or abnormal. Experiments conducted on real-world time-series data demonstrate the effectiveness of our proposed framework.
ER -
English
