Abstract: An algorithm for intrusion detection based on evolutionary semi-supervised fuzzy clustering is proposed which is suited for situation that gaining labeled data is more difficulty than unlabeled data in intrusion detection systems.The algorithm requires a small number of labeled data only and a large number of unlabeled dada，and class labels information provided by labeled data is used to guide the evolution process of each fuzzy partition on unlabeled data，which plays the role of chromosome.This algorithm can deal with fuzzy label，uneasily plunges locally optima and is suited to implement on parallel architecture.Experiments show that the algorithm can improve classification accuracy and has high detection efficiency.

Key words: intrsion detection, sem-supervised learning, clustering, evolutionary programming

摘要： 在入侵检测系统中，未知标签数据容易获得，标签数据较难获得，对此提出了一种基于进化半监督式模糊聚类入侵检测算法。算法利用标签数据信息担任染色体的角色，引导非标签数据每个模糊分类的进化过程，能够使用少量的标签数据和大量未知标签数据生成入侵检测系统分类器，可处理模糊类标签，不易陷入局部最优，适合并行结构的实现。实验结果表明，算法有较高的检测率。

关键词: 入侵检测, 半监督学习, 聚类, 进化规划