AnomalyDetect: An online distance-based anomaly detection algorithm

Wunjun Huo; Wei Wang; Wen Li

doi:10.1007/978-3-030-23499-7_5

AnomalyDetect: An online distance-based anomaly detection algorithm

Wunjun Huo, Wei Wang, Wen Li

School of Data Science and Engineering

Tongji University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

16 Scopus citations

Abstract

Anomaly detection is a key challenge in data mining, which refers to finding patterns in data that do not conform to expected behavior. It has a wide range of applications in many fields as diverse as finance, medicine, industry, and the Internet. In particular, intelligent operation has made great progress in recent years and has an urgent need for this technology. In this paper, we study the problem of anomaly detection in the context of intelligent operation and find the practical need for high-accuracy, online and universal anomaly detection algorithms in time series database. Based on the existing algorithms, we propose an innovative online distance-based anomaly detection algorithm. K-means and time-space trade-off mechanism are used to reduce the time complexity. Through the experiments on Yahoo! Web-scope S5 dataset we show that our algorithm can detect anomalies accurately. The comparative study of several anomaly detectors verifies the effectiveness and generality of the proposed algorithm.

Original language	English
Title of host publication	Web Services – ICWS 2019 - 26th International Conference, Held as Part of the Services Conference Federation, SCF 2019, Proceedings
Editors	John Miller, Eleni Stroulia, Kisung Lee, Liang-Jie Zhang
Publisher	Springer Verlag
Pages	63-79
Number of pages	17
ISBN (Print)	9783030234980
DOIs	https://doi.org/10.1007/978-3-030-23499-7_5
State	Published - 2019
Event	26th International Conference on Web Services, ICWS 2019 held as part of the Services Conference Federation, SCF 2019 - San Diego, United States Duration: 25 Jun 2019 → 30 Jun 2019

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11512 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	26th International Conference on Web Services, ICWS 2019 held as part of the Services Conference Federation, SCF 2019
Country/Territory	United States
City	San Diego
Period	25/06/19 → 30/06/19

Keywords

Anomaly detection
Euclidean distance
Intelligent operation
Online algorithm
Time series

Access to Document

10.1007/978-3-030-23499-7_5

Cite this

Huo, W., Wang, W., & Li, W. (2019). AnomalyDetect: An online distance-based anomaly detection algorithm. In J. Miller, E. Stroulia, K. Lee, & L.-J. Zhang (Eds.), Web Services – ICWS 2019 - 26th International Conference, Held as Part of the Services Conference Federation, SCF 2019, Proceedings (pp. 63-79). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11512 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-030-23499-7_5

Huo, Wunjun ; Wang, Wei ; Li, Wen. / AnomalyDetect : An online distance-based anomaly detection algorithm. Web Services – ICWS 2019 - 26th International Conference, Held as Part of the Services Conference Federation, SCF 2019, Proceedings. editor / John Miller ; Eleni Stroulia ; Kisung Lee ; Liang-Jie Zhang. Springer Verlag, 2019. pp. 63-79 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "Anomaly detection is a key challenge in data mining, which refers to finding patterns in data that do not conform to expected behavior. It has a wide range of applications in many fields as diverse as finance, medicine, industry, and the Internet. In particular, intelligent operation has made great progress in recent years and has an urgent need for this technology. In this paper, we study the problem of anomaly detection in the context of intelligent operation and find the practical need for high-accuracy, online and universal anomaly detection algorithms in time series database. Based on the existing algorithms, we propose an innovative online distance-based anomaly detection algorithm. K-means and time-space trade-off mechanism are used to reduce the time complexity. Through the experiments on Yahoo! Web-scope S5 dataset we show that our algorithm can detect anomalies accurately. The comparative study of several anomaly detectors verifies the effectiveness and generality of the proposed algorithm.",

keywords = "Anomaly detection, Euclidean distance, Intelligent operation, Online algorithm, Time series",

author = "Wunjun Huo and Wei Wang and Wen Li",

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year = "2019",

doi = "10.1007/978-3-030-23499-7\_5",

language = "英语",

isbn = "9783030234980",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

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Huo, W, Wang, W & Li, W 2019, AnomalyDetect: An online distance-based anomaly detection algorithm. in J Miller, E Stroulia, K Lee & L-J Zhang (eds), Web Services – ICWS 2019 - 26th International Conference, Held as Part of the Services Conference Federation, SCF 2019, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11512 LNCS, Springer Verlag, pp. 63-79, 26th International Conference on Web Services, ICWS 2019 held as part of the Services Conference Federation, SCF 2019, San Diego, United States, 25/06/19. https://doi.org/10.1007/978-3-030-23499-7_5

AnomalyDetect: An online distance-based anomaly detection algorithm. / Huo, Wunjun; Wang, Wei; Li, Wen.
Web Services – ICWS 2019 - 26th International Conference, Held as Part of the Services Conference Federation, SCF 2019, Proceedings. ed. / John Miller; Eleni Stroulia; Kisung Lee; Liang-Jie Zhang. Springer Verlag, 2019. p. 63-79 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11512 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - AnomalyDetect

T2 - 26th International Conference on Web Services, ICWS 2019 held as part of the Services Conference Federation, SCF 2019

AU - Huo, Wunjun

AU - Wang, Wei

AU - Li, Wen

N1 - Publisher Copyright: © Springer Nature Switzerland AG 2019.

PY - 2019

Y1 - 2019

N2 - Anomaly detection is a key challenge in data mining, which refers to finding patterns in data that do not conform to expected behavior. It has a wide range of applications in many fields as diverse as finance, medicine, industry, and the Internet. In particular, intelligent operation has made great progress in recent years and has an urgent need for this technology. In this paper, we study the problem of anomaly detection in the context of intelligent operation and find the practical need for high-accuracy, online and universal anomaly detection algorithms in time series database. Based on the existing algorithms, we propose an innovative online distance-based anomaly detection algorithm. K-means and time-space trade-off mechanism are used to reduce the time complexity. Through the experiments on Yahoo! Web-scope S5 dataset we show that our algorithm can detect anomalies accurately. The comparative study of several anomaly detectors verifies the effectiveness and generality of the proposed algorithm.

AB - Anomaly detection is a key challenge in data mining, which refers to finding patterns in data that do not conform to expected behavior. It has a wide range of applications in many fields as diverse as finance, medicine, industry, and the Internet. In particular, intelligent operation has made great progress in recent years and has an urgent need for this technology. In this paper, we study the problem of anomaly detection in the context of intelligent operation and find the practical need for high-accuracy, online and universal anomaly detection algorithms in time series database. Based on the existing algorithms, we propose an innovative online distance-based anomaly detection algorithm. K-means and time-space trade-off mechanism are used to reduce the time complexity. Through the experiments on Yahoo! Web-scope S5 dataset we show that our algorithm can detect anomalies accurately. The comparative study of several anomaly detectors verifies the effectiveness and generality of the proposed algorithm.

KW - Anomaly detection

KW - Euclidean distance

KW - Intelligent operation

KW - Online algorithm

KW - Time series

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U2 - 10.1007/978-3-030-23499-7_5

DO - 10.1007/978-3-030-23499-7_5

M3 - 会议稿件

AN - SCOPUS:85068232764

SN - 9783030234980

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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EP - 79

BT - Web Services – ICWS 2019 - 26th International Conference, Held as Part of the Services Conference Federation, SCF 2019, Proceedings

A2 - Miller, John

A2 - Stroulia, Eleni

A2 - Lee, Kisung

A2 - Zhang, Liang-Jie

PB - Springer Verlag

Y2 - 25 June 2019 through 30 June 2019

ER -

Huo W, Wang W, Li W. AnomalyDetect: An online distance-based anomaly detection algorithm. In Miller J, Stroulia E, Lee K, Zhang LJ, editors, Web Services – ICWS 2019 - 26th International Conference, Held as Part of the Services Conference Federation, SCF 2019, Proceedings. Springer Verlag. 2019. p. 63-79. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-23499-7_5

AnomalyDetect: An online distance-based anomaly detection algorithm

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Keywords

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