Abstract
In the computer network intrusion detection system, data objects, mapped from original space, are analyzed based on kernel clustering algorithm. During the process of kernel clustering, some representative points are introduced to represent a cluster. In just one iteration, the distance between a data object and representative points of a cluster is computed to partition the data objects. The clustering results contain normal data and abnormal data, which achieve the goal of intrusion detection. At the same time, KDD CUP 1999 dataset is used to make simulations. The results show that the proposed algorithm has higher detecting probability under the condition of low constant false alarm rate compared with K-Means clustering algorithm and SVM.
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