The Improved K-Means Algorithm in Intrusion Detection System Research

Article Preview

Abstract:

To improve the efficiency of Internet intrusion detection, data mining is adopted in intrusion detection. The paper introduces the concept of intrusion detection and k-means algorithm. For the defect of K-means algorithm, it proposes an improved K-means algorithm. Experiments show that the improved k-means algorithm can get a better detection rate.

You have full access to the following eBook

Info:

Periodical:

Pages:

204-208

Citation:

Online since:

September 2011

Export:

Share:

Citation:

[1] Zhu Ming. Data Mining. Press of University of Science and Technology. (2008).

Google Scholar

[2] Xue Jingfeng , Cao Yuanda. Intrusion Detection Based on Data Mining. Computer Engineering. 2003, Vol. 29, No. 3. 17~19.

Google Scholar

[3] Domeniconi C , Papadopoulos D , Gunopulos D , Ma S1 Subspace Clustering of High Dimensional Data In : Proc. of the Fourth SI- AM Intl. Conf. on Data Mining , 2004. 517~521.

DOI: 10.1137/1.9781611972740.58

Google Scholar

[4] Wang Xizhao , Wang Yadong , Wang Lijuan. Improving fuzzy c- means clustering based on feature-weight learning. Pattern Recognition Letters , 2004 , 25 : 1123~1132.

DOI: 10.1016/j.patrec.2004.03.008

Google Scholar

[5] Yuan Fang, Zhou Zhiyong, Song Xin. K-means Clustering Algorithm with Meliorated Initial Center. Computer Engineering, 2007, Vol. 33 , No. 3. 65~66.

Google Scholar

[6] Ren Jiangtao, Shi Xiaoxiao, Sun Jinhao. An Improved K-Means Clustering Algorithm Based on Feature Weighting. Computer Science. 2006Vol133, No 17. 186~187.

Google Scholar

[7] Huang Zhexue. Extensions to t he K-Means Algorithm for Clustering LargeData Sets with Categorical Values. Data Mining and Knowledge Discovery , 1998. 283~304.

Google Scholar