Volume 9 Number 7 (Jul. 2014)
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JCP 2014 Vol.9(7): 1542-1546 ISSN: 1796-203X
doi: 10.4304/jcp.9.7.1542-1546

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

Jingping Song1, 2, Zhiliang Zhu1, Peter Scully2, Chris Price2
1Software College, Northeastern University, Shenyang, China, 110819
2Department of Computer Science, Aberystwyth University, Aberystwyth, United Kingdom, SY23 3DB


Abstract—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

Index Terms—Feature selection, classification, C4.5, intrusion detection, mutual information

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Cite: Jingping Song, Zhiliang Zhu, Peter Scully, Chris Price, "Modified Mutual Information-based Feature Selection for Intrusion Detection Systems in Decision Tree Learning," Journal of Computers vol. 9, no. 7, pp. 1542-1546, 2014.

General Information

ISSN: 1796-203X
Abbreviated Title: J.Comput.
Frequency: Bimonthly
Editor-in-Chief: Prof. Liansheng Tan
Executive Editor: Ms. Nina Lee
Abstracting/ Indexing: DBLP, EBSCO,  ProQuest, INSPEC, ULRICH's Periodicals Directory, WorldCat, CNKI,etc
E-mail: jcp@iap.org
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