Modified Mutual Information-based Feature Selection for Intrusion Detection Systems in Decision Tree Learning

被引：8

作者：

Song, Jingping ^{[1
,2
]}

Zhu, Zhiliang ^{[1
]}

Scully, Peter ^{[2
]}

Price, Chris ^{[2
]}

机构：

[1] Northeastern Univ, Software Coll, Shenyang 110819, Liaoning, Peoples R China

[2] Aberystwyth Univ, Dept Comp Sci, Aberystwyth SY23 3DB, Dyfed, Wales

来源：

JOURNAL OF COMPUTERS | 2014年 / 9卷 / 07期

关键词：

Feature selection; classification; C4.5; intrusion detection; mutual information;

D O I：

10.4304/jcp.9.7.1542-1546

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

As network-based technologies become omnipresent, intrusion detection and prevention for these systems become increasingly important. This paper proposed a modified mutual information-based feature selection algorithm (MMIFS) for intrusion detection on the KDD Cup 99 dataset. The C4.5 classification method was used with this feature selection method. In comparison with dynamic mutual information feature selection algorithm (DMIFS), we can see that most performance aspects are improved. Furthermore, this paper shows the relationship between performance, efficiency and the number of features selected.

引用

页码：1542 / 1546

页数：5

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