Selecting Features for Anomaly Intrusion Detection: A Novel Method using Fuzzy C Means and Decision Tree Classification

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

Research output: Chapter in Book/Report/Conference proceedingConference Proceeding (Non-Journal item)

13 Citations (SciVal)

Abstract

In this work, a new method for classification is proposed consisting of a combination of feature selection, normalization, fuzzy C means clustering algorithm and C4.5 decision tree algorithm. The aim of this method is to improve the performance of the classifier by using selected features. The fuzzy C means clustering method is used to partition the training instances into clusters. On each cluster, we build a decision tree using C4.5 algorithm. Experiments on the KDD CUP 99 data set shows that our proposed method in detecting intrusion achieves better performance while reducing the relevant features by more than 80%.
Original languageEnglish
Title of host publicationCyberspace Safety and Security
Subtitle of host publicationProceedings 5th International Symposium, CSS 2013, Zhangjiajie, China, November 13-15, 2013
EditorsGuojun Wang
PublisherSpringer Nature
Pages299-307
Volume8300
ISBN (Electronic)978-3-319-03584-0
ISBN (Print)978-3-319-03583-3, 3319035835
Publication statusPublished - 11 Nov 2013

Publication series

NameLecture Notes in Computer Science / Security and Cryptology
PublisherSpringer

Keywords

  • Intrusion detection
  • Fuzzy C-Means
  • Feature selection
  • C4.5

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