Selecting Features for Anomaly Intrusion Detection:A Novel Method using Fuzzy C Means and Decision Tree Classification

摘要：

　　In this work, a new method for classification is proposed consisting of a combination of feature selection, normalization, fuzzy C means clustering algorithm and C4.5 decision tree algorithm.The aim of this method is to improve the performance of the classifier by using selected features.The fuzzy C means clustering method is used to partition the training instances into clusters.On each cluster, we build a decision tree using C4.5 algorithm.Experiments on the KDD CUP 99 data set shows that our proposed method in detecting intrusion achieves better performance while reducing the relevant features by more than 80%.

关键词： Intrusion detection Fuzzy C-Means Feature selection C4.5

作者: Jingping Song Zhiliang Zhu Peter Scully Chris Price

作者单位: Software College of Northeastern University,Shenyang,Liaoning,China,110819;Department of Computer Sc Software College of Northeastern University,Shenyang,Liaoning,China,110819 Department of Computer Science,Aberystwyth University,UK,SY23 3DB

会议类型: 国际会议

会议名称: The 5th International Symposium on Cyberspace Safety and Security ( CSS2013)(第五届国际网络空间安全和安保研讨会)

会议地点: 张家界

会议语种:英文

页码: 299-307

在线出版日期: 2013-11-13（万方平台首次上网日期，不代表论文的发表时间）