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1、数据异常的监测技术综述吴镜锋 ;金炜东 ;唐鹏【期刊名称】计算机科学【年(卷),期】2017(044)0z2【摘要】 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
2、many fields,such as machine fault detection,data mining and disease detection and intrusiondetection.Basedon a largenumberof anomalydetectionmethods at present,this paper mainly discussed the existence of anomalydata,classifiedthe majoranomaly detectionmethodsaccording to this framework,and put forw
3、ard 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.% 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