期刊名称：International Journal of Security and Its Applications
印刷版ISSN：1738-9976
出版年度：2014
卷号：8
期号：5
页码：311-322
DOI：10.14257/ijsia.2014.8.5.28
出版社：SERSC
摘要：In this paper, we propose a multi-layer selection and mining methods for effective intrusion detection, which utilize feature selection, classification, clustering and evidence theory for decision making. In the experiments, DARPA KDD-99 intrusion detection data set is used for evaluation. It shows that our proposed classifier not only classifies and separates the normal and abnormal data, but also reduces false positive and false negative besides detecting all four kinds of attacks.
