Selecting Features for Anomaly Intrusion Detection: A Novel Method using Fuzzy C Means and Decision Tree Classification
作者： Jingping Song , Zhiliang Zhu , Peter Scully , Chris Price
DOI: 10.1007/978-3-319-03584-0_22
关键词: Normalization (statistics) 、 Mathematics 、 Data mining 、 Decision tree 、 Intrusion detection system 、 Decision tree learning 、 Cluster analysis 、 Artificial intelligence 、 Fuzzy logic 、 Feature selection 、 Pattern recognition 、 Classifier (UML)
摘要: In this work, a new method for classification is proposed consisting of combination feature selection, normalization, fuzzy C means clustering algorithm and C4.5 decision tree algorithm. The aim to improve the performance classifier by using selected features. used partition training instances into clusters. On each cluster, we build Experiments on KDD CUP 99 data set shows that our in detecting intrusion achieves better while reducing relevant features more than 80%.
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