数据异常的监测技术综述

doi:10.11896/j.issn.1002-137X.2017.11A.004

摘要/Abstract

摘要： 在目前大数据的环境下,相对于正常数据,异常类数据更难获取,也显得更加重要。异常检测的目的是检测出异于正常主体的活动数据。异常检测适用于机器故障诊断、数据挖掘以及疾病和入侵检测等多个领域。基于目前大量的异常检测方法,主要从异常类数据的有无来阐述,根据这个框架将主要的异常检测方法进行了分类,并评价了这些方法的优劣；最后重点讨论了基于深度学习的大数据异常检测方法,并分别介绍了不同的方法及相关的应用和未来的研究热点。

关键词: 异常检测,监督学习,无监督学习,深度学习

Abstract: In the current environment of large data,anomaly data is more difficult to obtain than normal data,and is more important.The purpose of anomaly detection is to detect activity data from normal subjects.Anomaly detection is widely applied in many fields,such as machine fault detection,data mining and disease detection and intrusion detection.Based on a large number of anomaly detection methods at present,this paper mainly discussed the existence of anomaly data,classified the major anomaly detection methods according to this framework,and put forward the advantages and disadvantages of these methods.Finally,we focused on the large data anomaly detection methods based on the deep learning,and introduced different methods and related applications and future research hotpots respectively.

Key words: Anomaly detection,Supervised learning,Unsupervised learning,Deep learning

吴镜锋,金炜东,唐鹏. 数据异常的监测技术综述[J]. 计算机科学, 2017, 44(Z11): 24-28. https://doi.org/10.11896/j.issn.1002-137X.2017.11A.004

WU Jing-feng, JIN Wei-dong and TANG Peng. Survey on Monitoring Techniques for Data Abnormalities[J]. Computer Science, 2017, 44(Z11): 24-28. https://doi.org/10.11896/j.issn.1002-137X.2017.11A.004

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