基于聚类分析的数据流异常检测  

Anomaly detection of data stream based on clustering analysis

作　　者：尚志远[1] 张岳公[1] 张诺诺[1] 

机构地区：[1]山东大学计算机科学与技术学院,济南中国250101

出　　处：《中国电子商情（通信市场）》2011年第6期328-334,共7页

摘　　要：近期，随着诸如实时监控系统、网络入侵检测和web上用户点击流等动态的应用环境源源不断地产生海量的、时序的、快速变化的和潜在无限的数据流，对数据流的异常检测研究变得重要而富有意义。数据流聚类是数据挖掘领域的研究热点，在近期被高度重视和广泛研究。本文建立了一个通用的基于数据流聚类分析的异常检测模型，通过改进数据流聚类算法CluStream，提出了适合数据流异常检测的算法ACluStream，利用其联机微聚类处理对数据流进行聚类，并按金字塔时间框架保存聚类特征信息，再使用离线宏聚类处理检测出异常数据流。实验结果证明，该模型能较好地应用于数据流的异常检测。The research to data streaming model has recently gained a high attraction due to its applications, including real-time surveillance systems, network intrusion detection and click streams. Clustering based on data streaming, one of the most important in data mining, has recently been highly explored because its application to data summarization and outlier detection. This paper established a general model of the anomaly detection based on clustering analysis of data stream. Through improving CluStream which is Clustering algorithm of data stream, we present ACluStream algorithm for anomaly detection of data stream. The stream clustering approach is separated into online components which cluster stream and store clustering features according to Pyramidal time frame and offline components which detect anomaly of data stream. The experiment results show that the proposed algorithms and models are very effective to anomaly detection of data stream.

关 键 词：数据流 聚类 异常检测 

分 类 号：TP393.08[自动化与计算机技术—计算机应用技术]