Recent Progress of Anomaly Detection

Author:

Xu Xiaodan12,Liu Huawen13,Yao Minghai2ORCID

Affiliation:

1. Department of Computer Science, Zhejiang Normal University, Jinhua 321004, China

2. College of Information Engineering, Zhejiang University of Technology, Hangzhou, 310000, China

3. Shanghai Key Laboratory of Intelligent Information Processing, Shanghai 200433, China

Abstract

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.

Funder

National Natural Science Foundation of China

Publisher

Hindawi Limited

Subject

Multidisciplinary,General Computer Science

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