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

Jingping Song, Zhiliang Zhu, Peter Matthew David Scully, Christopher Price

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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.
Original languageEnglish
Pages (from-to)1542-1546
JournalJournal of Computers
Volume9
Issue number7
DOIs
Publication statusPublished - Jul 2014

Keywords

  • feature selection
  • classification
  • C4.5
  • intrustion detection
  • mutual information

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