Classification via k-Means Clustering and Distance-Based Outlier Detection

被引：0

作者：

Songma, Surasit ^{[1
]}

Chimphlee, Witcha ^{[2
]}

Maichalernnukul, Kiattisak ^{[1
]}

Sanguansat, Parinya ^{[3
]}

机构：

[1] Rangsit Univ, Fac Informat Technol, Pathum Thani, Thailand

[2] Suan Dusit Rajabhat Univ, Fac Sci & Technol, Bangkok, Thailand

[3] Panyapiwat Inst Management, Fac Engn & Technol, Nonthaburi, Thailand

来源：

2012 TENTH INTERNATIONAL CONFERENCE ON ICT AND KNOWLEDGE ENGINEERING | 2012年

关键词：

Classification; k-means; intrusion detection; KDD Cup 1999 data set; outlier detection;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

We propose a two-phase classification method. Specifically, in the first phase, a set of patterns (data) are clustered by the k-means algorithm. In the second phase, outliers are constructed by a distance-based technique and a class label is assigned to each pattern. The Knowledge Discovery Databases (KDD) Cup 1999 data set, which has been utilized extensively for development of intrusion detection systems, is used in our experiment. The results show that the proposed method is effective in intrusion detection.

引用

页码：125 / 128

页数：4