关键词: 入侵检测, 半监督聚类, 异构数据

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 density 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 dataset, and experimental results show that the method improves the detection accuracy.

Key words: intrusion detection, semi-supervised clustering, heterogeneous datasets