计算机应用与软件2011，Vol.28Issue(3)：282-284,299,4.
基于人工免疫网络和模糊C-均值聚类的入侵检测方法
INTRUSION DETECTION BASED ON ARTIFICIAL IMMUNE NETWORK AND FUZZY C-MEANS CLUSTERING
李丽娟 1唐文纪1
作者信息
- 1. 湖南大学计算机与通信学院,湖南,长沙,410082
- 折叠
摘要
Abstract
The FCM clustering algorithm in intrusion detection method has two main shortcomings: sensitive to initial values and asking to input the number of clustering. In order to solve these two shortcomings, the intrusion detection method based on artificial immune network and fuzzy c-means algorithm is proposed by applying artificial immune network algorithm to FCM clustering algorithm. Through the simulation experiments on KDD_CUP99 data sets, the algorithm improves the detection rate and reduces the false alarm rate compared with the FCM algorithm. Experimental results show that this method can effectively detect intrusions in the networks.关键词
入侵检测/模糊C-均值算法/人工免疫网络算法引用本文复制引用
李丽娟,唐文纪..基于人工免疫网络和模糊C-均值聚类的入侵检测方法[J].计算机应用与软件,2011,28(3):282-284,299,4.
