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

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 seri...

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Bibliographic Details
Main Authors: Wang Huai-bin, Yang Hong-liang, Xu Zhi-jian, Yuan Zheng
Format: Conference Proceeding
Language:English
Subjects:
Algorithm design and analysis
Artificial neural networks
Clustering algorithms
IDS(Intrusion Detection System)
Image color analysis
Intrusion detection
K-Means
SOM(Self-Organizing Map)
Testing
Training
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Summary: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.
DOI:10.1109/ICEE.2010.327