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计算机工程 ›› 2007, Vol. 33 ›› Issue (15): 172-174. doi: 10.3969/j.issn.1000-3428.2007.15.061

• 人工智能及识别技术 • 上一篇    下一篇

数据流中孤立点识别方法

单世民1,邓贵仕1,何英昊2   

  1. (1. 大连理工大学系统工程研究所,大连 116023;2. 大连理工大学城市学院,大连 116600)
  • 收稿日期:1900-01-01 修回日期:1900-01-01 出版日期:2007-08-05 发布日期:2007-08-05

Online Detection Method Towards Outlier in Data Stream

SHAN Shi-min1, DENG Gui-shi1, HE Ying-hao2   

  1. (1. Institute of Systems Engineering, Dalian University of Technology, Dalian 116023; 2. School of City, Dalian University of Technology, Dalian 116600)
  • Received:1900-01-01 Revised:1900-01-01 Online:2007-08-05 Published:2007-08-05

摘要: 针对基于聚类分析及基于孤立点检测的入侵检测方法的局限,根据数据流的特点,提出了一种数据流中孤立点动态识别方法。该方法使用动态微粒群算法对特征空间中当前主要聚类的特征点进行追踪,通过计算数据流中数据对象与特征点的距离来判断数据对象的性质。将该方法应用于入侵检测而进行的实验证明了方法的有效性。

关键词: 数据流, 聚类, 孤立点检测, 微粒群算法, 入侵检测

Abstract: The is paper presents a novel online detection method towards the outlier in data stream. The method is referred as online detection towards outliers in data stream(ODODS). The method is partially inspired by the density based clustering and uses improved adaptive particle swarm optimizer(IAPSO) for detecting the outliers. Results of experiments implemented in intrusion detection domain prove the efficiency of ODODS.

Key words: data stream, clustering, outlier detection, PSO, intrusion detection

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