NDLI: A Clustering Algorithm Use SOM and K-Means in Intrusion Detection

Content Provider	IEEE Xplore Digital Library
Author	Wang Huai-bin Yang Hong-liang Xu Zhi-jian Yuan Zheng
Copyright Year	2010
Abstract	Improving detection definition is a pivotal problem for intrusion detection. Many intelligent algorithms were used to improve the detection rate and reduce the false rate. Traditional SOM cannot provide the precise clustering results to us, while traditional K-Means depends on the initial value serious and it is difficult to find the center of cluster easily. Therefore, in this paper we introduce a new algorithm, first, we use SOM gained roughly clusters and center of clusters, then, using K-Means refine the clustering in the SOM stage. At last of this paper we take KDD CUP-99 dataset to test the performance of the new algorithm. The new algorithm overcomes the defects of traditional algorithms effectively. Experimental results show that the new algorithm has a good stability of efficiency and clustering accuracy.
Starting Page	1281
Ending Page	1284
File Size	349374
Page Count	4
File Format	PDF
e-ISBN	9781424466474
DOI	10.1109/ICEE.2010.327
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-05-07
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Algorithm design and analysis Training SOM(Self-Organizing Map) Image color analysis K-Means Clustering algorithms Intrusion detection Artificial neural networks IDS(Intrusion Detection System) Testing
Content Type	Text
Resource Type	Article

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