一种基于云模型和半监督聚类的入侵检测算法

入侵检测是网络安全防御体系的关键技术之一,针对目前网络入侵检测率低、误报率高的问题,提出了一种将云模型和半监督聚类相结合的入侵检测算法。由于属性对分类贡献程度不同,引入了云相对贴近度的概念,给出了计算属性权重的方法。以改进的聚类方法为基础建立云模型,对属性使用动态加权和更新云模型的方法逐渐强化分类器指导数据的分类。通过KDD CUP99实验数据的仿真,实验结果证明了该算法的有效性。

New intrusion detection algorithm based on cluster and cloud model

Intrusion detection system is one of key technologies in network security system. Aiming at the problem of low detection rate and high false alarm rate in network intrusion detection,a new intrusion detection algorithm based on cluster and cloud model is proposed. Because of the different contribution of the attribute to the classification,the attribute is leaded into based on the concept of ＂clouds approach degree＂. The cloud model is built based on the improved cluster in the text. Using the method of dynamic weighted the attribute and to update the cloud model,the classifier to guide the data classification is gradually strengthened. KDD CUP99 data set is implemented to evaluate the proposed algorithm. The experimental results prove that the method is feasible and effective.