Recent Progress of Anomaly Detection
Anomaly analysis is of great interest to diverse fields, including data mining and machine learning, and plays a critical role in a wide range of applications, such as medical health, credit card fraud, and intrusion detection. Recently, a significant number of anomaly detection methods with a varie...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2019-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2019/2686378 |
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| _version_ | 1832545830069862400 |
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| author | Xiaodan Xu Huawen Liu Minghai Yao |
| author_facet | Xiaodan Xu Huawen Liu Minghai Yao |
| author_sort | Xiaodan Xu |
| collection | DOAJ |
| description | Anomaly analysis is of great interest to diverse fields, including data mining and machine learning, and plays a critical role in a wide range of applications, such as medical health, credit card fraud, and intrusion detection. Recently, a significant number of anomaly detection methods with a variety of types have been witnessed. This paper intends to provide a comprehensive overview of the existing work on anomaly detection, especially for the data with high dimensionalities and mixed types, where identifying anomalous patterns or behaviours is a nontrivial work. Specifically, we first present recent advances in anomaly detection, discussing the pros and cons of the detection methods. Then we conduct extensive experiments on public datasets to evaluate several typical and popular anomaly detection methods. The purpose of this paper is to offer a better understanding of the state-of-the-art techniques of anomaly detection for practitioners. Finally, we conclude by providing some directions for future research. |
| format | Article |
| id | doaj-art-0d035198842d4b3987838b65fa2ef5e8 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2019-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-0d035198842d4b3987838b65fa2ef5e82025-02-03T07:24:30ZengWileyComplexity1076-27871099-05262019-01-01201910.1155/2019/26863782686378Recent Progress of Anomaly DetectionXiaodan Xu0Huawen Liu1Minghai Yao2Department of Computer Science, Zhejiang Normal University, Jinhua 321004, ChinaDepartment of Computer Science, Zhejiang Normal University, Jinhua 321004, ChinaCollege of Information Engineering, Zhejiang University of Technology, Hangzhou, 310000, ChinaAnomaly analysis is of great interest to diverse fields, including data mining and machine learning, and plays a critical role in a wide range of applications, such as medical health, credit card fraud, and intrusion detection. Recently, a significant number of anomaly detection methods with a variety of types have been witnessed. This paper intends to provide a comprehensive overview of the existing work on anomaly detection, especially for the data with high dimensionalities and mixed types, where identifying anomalous patterns or behaviours is a nontrivial work. Specifically, we first present recent advances in anomaly detection, discussing the pros and cons of the detection methods. Then we conduct extensive experiments on public datasets to evaluate several typical and popular anomaly detection methods. The purpose of this paper is to offer a better understanding of the state-of-the-art techniques of anomaly detection for practitioners. Finally, we conclude by providing some directions for future research.http://dx.doi.org/10.1155/2019/2686378 |
| spellingShingle | Xiaodan Xu Huawen Liu Minghai Yao Recent Progress of Anomaly Detection Complexity |
| title | Recent Progress of Anomaly Detection |
| title_full | Recent Progress of Anomaly Detection |
| title_fullStr | Recent Progress of Anomaly Detection |
| title_full_unstemmed | Recent Progress of Anomaly Detection |
| title_short | Recent Progress of Anomaly Detection |
| title_sort | recent progress of anomaly detection |
| url | http://dx.doi.org/10.1155/2019/2686378 |
| work_keys_str_mv | AT xiaodanxu recentprogressofanomalydetection AT huawenliu recentprogressofanomalydetection AT minghaiyao recentprogressofanomalydetection |