关键词: 网络入侵检测, 模糊核聚类, 支持向量

Abstract: To solve the problem of high cost in labeling the data artificially and that of the dimension effect by traditional clustering method, this paper proposes a new fuzzy support vector clustering algorithm to cope with unlabeled data. Through combining K-means and DBSCAN algorithm to generate association matrix, setting the threshold value of constraint term to get the initial clustering, and using the fuzzy support vector domain description, the final result is achieved. The contrast experiment shows the feasibility and effectiveness of this method.

Key words: network intrusion detection, fuzzy kernel clustering, support vector