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

^*此作品的通讯作者

数据科学与工程学院

Tongji University

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

17 引用（Scopus）

摘要

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.

源语言	英语
主期刊名	Web Services – ICWS 2019 - 26th International Conference, Held as Part of the Services Conference Federation, SCF 2019, Proceedings
编辑	John Miller, Eleni Stroulia, Kisung Lee, Liang-Jie Zhang
出版商	Springer Verlag
页	63-79
页数	17
ISBN（印刷版）	9783030234980
DOI	https://doi.org/10.1007/978-3-030-23499-7_5
出版状态	已出版 - 2019
活动	26th International Conference on Web Services, ICWS 2019 held as part of the Services Conference Federation, SCF 2019 - San Diego, 美国期限: 25 6月 2019 → 30 6月 2019

出版系列

姓名	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
卷	11512 LNCS
ISSN（印刷版）	0302-9743
ISSN（电子版）	1611-3349

会议

会议	26th International Conference on Web Services, ICWS 2019 held as part of the Services Conference Federation, SCF 2019
国家/地区	美国
市	San Diego
时期	25/06/19 → 30/06/19

访问文件

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

其它文件与链接

链接到 Scopus 的出版物

引用此

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

@inproceedings{354adbd377f84f328bfe8797184ad0f1,

title = "AnomalyDetect: An online distance-based anomaly detection algorithm",

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",

note = "Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2019.; 26th International Conference on Web Services, ICWS 2019 held as part of the Services Conference Federation, SCF 2019 ; Conference date: 25-06-2019 Through 30-06-2019",

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)",

publisher = "Springer Verlag",

pages = "63--79",

editor = "John Miller and Eleni Stroulia and Kisung Lee and Liang-Jie Zhang",

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

address = "德国",

}

Huo, W, Wang, W & Li, W 2019, AnomalyDetect: An online distance-based anomaly detection algorithm. 在 J Miller, E Stroulia, K Lee & L-J Zhang (编辑), 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), 卷 11512 LNCS, Springer Verlag, 页码 63-79, 26th International Conference on Web Services, ICWS 2019 held as part of the Services Conference Federation, SCF 2019, San Diego, 美国, 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. 编辑 / John Miller; Eleni Stroulia; Kisung Lee; Liang-Jie Zhang. Springer Verlag, 2019. 页码 63-79 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); 卷 11512 LNCS).

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

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

UR - https://www.scopus.com/pages/publications/85068232764

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)

SP - 63

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. 在 Miller J, Stroulia E, Lee K, Zhang LJ, 编辑, Web Services – ICWS 2019 - 26th International Conference, Held as Part of the Services Conference Federation, SCF 2019, Proceedings. Springer Verlag. 2019. 页码 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

摘要

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