incremental outlier detection in data streams using local correlation integral

	incremental outlier detection in data streams using local correlation integral
	Lu Xinjie; Yang Tian; Liao Zaifei; Elahi Manzoor; Liu Wei; Wang Hongan
	2009
会议名称	24th Annual ACM Symposium on Applied Computing, SAC 2009
会议录名称	Proceedings of the ACM Symposium on Applied Computing
会议日期	37323
会议地点	Honolulu, HI, United states
出版地	United States
ISBN	9781605581668
部门归属	(1) Graduate University, Chinese Academy of Sciences, Beijing, China; (2) Institute of Software, Chinese Academy of Sciences, Beijing, China; (3) State Key Lab. of Computer Science, Institute of Software, Chinese Academy of Sciences, Beijing, China
摘要	In this paper, an incremental outlier detection technique capable of dealing with a large amount of data is presented and evaluated in the context of intrusion detection. The proposed method is based on the LOcal Correlation Integral (LOCI for short). The detection technique consists of two parts. The first part named insertion receives the sequence of input point and updates Multi-granularity DEviation Factor (MDEF) of the point at intervals. The second part named deletion deletes one or a batch of points. This technique is able to process streaming data in a single scan. Moreover, the number of updates in the incremental LOCI algorithm per insertion/deletion of a single data record does not depend on the total number of data records. Experimental results with real life data sets show that the technique is capable of dealing with data streams, successfully detecting outlier. Copyright 2009 ACM.
关键词	Computer Science Data Communication Systems
主办者	ACM SIGAPP
内容类型	会议论文
URI标识	http://ir.iscas.ac.cn/handle/311060/8492
专题	人机交互技术与智能信息处理实验室
推荐引用方式 GB/T 7714	Lu Xinjie,Yang Tian,Liao Zaifei,et al. incremental outlier detection in data streams using local correlation integral[C]. United States,2009.