Anomaly detection using correctness matching through a neighborhood rough set

Pey Yun Goh; Shing Chiang Tan; Wooi Ping Cheah

doi:10.1007/978-3-319-46675-0_47

Anomaly detection using correctness matching through a neighborhood rough set

Pey Yun Goh, Shing Chiang Tan, Wooi Ping Cheah

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

1 Citation (Scopus)

Abstract

Abnormal information patterns are signals retrieved from a data source that could contain erroneous or reveal faulty behavior. Despite which signal it is, this abnormal information could affect the distribution of a real data. An anomaly detection method, i.e. Neighborhood Rough Set with Correctness Matching (NRSCM) is presented in this paper to identify abnormal information (outliers). Two real-life data sets, one mixed data and one categorical data, are used to demonstrate the performance of NRSCM. The experiments positively show good performance of NRSCM in detecting anomaly.

Original language	English
Title of host publication	Neural Information Processing - 23rd International Conference, ICONIP 2016, Proceedings
Editors	Akira Hirose, Minho Lee, Derong Liu, Kenji Doya, Kazushi Ikeda, Seiichi Ozawa
Publisher	Springer Verlag
Pages	434-441
Number of pages	8
ISBN (Print)	9783319466743
DOIs	https://doi.org/10.1007/978-3-319-46675-0_47
Publication status	Published - 2016
Externally published	Yes

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	9949 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Keywords

Anomaly detection
Neighborhood
Outlier detection
Rough set

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-319-46675-0_47

Cite this

Goh, P. Y., Tan, S. C., & Cheah, W. P. (2016). Anomaly detection using correctness matching through a neighborhood rough set. In A. Hirose, M. Lee, D. Liu, K. Doya, K. Ikeda, & S. Ozawa (Eds.), Neural Information Processing - 23rd International Conference, ICONIP 2016, Proceedings (pp. 434-441). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9949 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-46675-0_47

Goh, Pey Yun ; Tan, Shing Chiang ; Cheah, Wooi Ping. / Anomaly detection using correctness matching through a neighborhood rough set. Neural Information Processing - 23rd International Conference, ICONIP 2016, Proceedings. editor / Akira Hirose ; Minho Lee ; Derong Liu ; Kenji Doya ; Kazushi Ikeda ; Seiichi Ozawa. Springer Verlag, 2016. pp. 434-441 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "Abnormal information patterns are signals retrieved from a data source that could contain erroneous or reveal faulty behavior. Despite which signal it is, this abnormal information could affect the distribution of a real data. An anomaly detection method, i.e. Neighborhood Rough Set with Correctness Matching (NRSCM) is presented in this paper to identify abnormal information (outliers). Two real-life data sets, one mixed data and one categorical data, are used to demonstrate the performance of NRSCM. The experiments positively show good performance of NRSCM in detecting anomaly.",

keywords = "Anomaly detection, Neighborhood, Outlier detection, Rough set",

author = "Goh, {Pey Yun} and Tan, {Shing Chiang} and Cheah, {Wooi Ping}",

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Goh, PY, Tan, SC & Cheah, WP 2016, Anomaly detection using correctness matching through a neighborhood rough set. in A Hirose, M Lee, D Liu, K Doya, K Ikeda & S Ozawa (eds), Neural Information Processing - 23rd International Conference, ICONIP 2016, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9949 LNCS, Springer Verlag, pp. 434-441. https://doi.org/10.1007/978-3-319-46675-0_47

Anomaly detection using correctness matching through a neighborhood rough set. / Goh, Pey Yun; Tan, Shing Chiang; Cheah, Wooi Ping.
Neural Information Processing - 23rd International Conference, ICONIP 2016, Proceedings. ed. / Akira Hirose; Minho Lee; Derong Liu; Kenji Doya; Kazushi Ikeda; Seiichi Ozawa. Springer Verlag, 2016. p. 434-441 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9949 LNCS).

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

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T1 - Anomaly detection using correctness matching through a neighborhood rough set

AU - Goh, Pey Yun

AU - Tan, Shing Chiang

AU - Cheah, Wooi Ping

N1 - Publisher Copyright: © Springer International Publishing AG 2016.

PY - 2016

Y1 - 2016

N2 - Abnormal information patterns are signals retrieved from a data source that could contain erroneous or reveal faulty behavior. Despite which signal it is, this abnormal information could affect the distribution of a real data. An anomaly detection method, i.e. Neighborhood Rough Set with Correctness Matching (NRSCM) is presented in this paper to identify abnormal information (outliers). Two real-life data sets, one mixed data and one categorical data, are used to demonstrate the performance of NRSCM. The experiments positively show good performance of NRSCM in detecting anomaly.

AB - Abnormal information patterns are signals retrieved from a data source that could contain erroneous or reveal faulty behavior. Despite which signal it is, this abnormal information could affect the distribution of a real data. An anomaly detection method, i.e. Neighborhood Rough Set with Correctness Matching (NRSCM) is presented in this paper to identify abnormal information (outliers). Two real-life data sets, one mixed data and one categorical data, are used to demonstrate the performance of NRSCM. The experiments positively show good performance of NRSCM in detecting anomaly.

KW - Anomaly detection

KW - Neighborhood

KW - Outlier detection

KW - Rough set

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SN - 9783319466743

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

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BT - Neural Information Processing - 23rd International Conference, ICONIP 2016, Proceedings

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Goh PY, Tan SC, Cheah WP. Anomaly detection using correctness matching through a neighborhood rough set. In Hirose A, Lee M, Liu D, Doya K, Ikeda K, Ozawa S, editors, Neural Information Processing - 23rd International Conference, ICONIP 2016, Proceedings. Springer Verlag. 2016. p. 434-441. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-46675-0_47

Anomaly detection using correctness matching through a neighborhood rough set

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