计算机工程与应用2016，Vol.52Issue(3)：96-99,4.DOI:10.3778/j.issn.1002-8331.1402-0121
基于半监督模糊聚类的入侵检测
Semi-supervised fuzzy clustering algorithm for intrusion detection
摘要
Abstract
Because collecting labeled samples is more difficult than collecting unlabeled samples and network data include value attribute and symbol attribute, this paper proposes an improved semi-supervised fuzzy clustering algorithm based on heterogeneous distance and sample density for intrusion detection. The algorithm computes membership with sample den-sity of one class and heterogeneous distance of intrusion detection dataset. Then it computes distance between sample and the center of every class and sets sample belonging to class of min-distance. It makes experiment with KDDCUP99 datas-et, and experimental results show that the method improves the detection accuracy.关键词
入侵检测/半监督聚类/异构数据Key words
intrusion detection/semi-supervised clustering/heterogeneous datasets分类
信息技术与安全科学引用本文复制引用
杜红乐,樊景博..基于半监督模糊聚类的入侵检测[J].计算机工程与应用,2016,52(3):96-99,4.基金项目
陕西省教育厅科技计划项目(No.12JK0748) （No.12JK0748）
商洛学院科学与技术研究项目(No.13sky024). （No.13sky024）
