期刊名称：International Journal of Security and Its Applications
印刷版ISSN：1738-9976
出版年度：2016
卷号：10
期号：11
页码：1
出版社：SERSC
摘要：The diversity and concealment of network attack lead to the difficulty of network intrusion detection, in order to further improve the detection accuracy and efficiency of network intrusion detection, this article proposes a novel model FS-ELM, which is based on the combination of Fisher Score (FS) for feature selection and ELM classifiers for network intrusion detection. In the proposed model, FS is used to conduct feature selection to select the most distinguished feature subsets, and then to get diverse training subsets, in terms of these subsets, ELM classifiers are trained. Finally the results are achieved. Experiment on KDD CUP 99 data set, by means of the experimental analysis and comparison with SVM, LS-SVM and KNN, the proposed model not only improves the detection accuracy, but also enhances detection efficiency, it proves that it is an effective model for network intrusion detection.
