Building an intrusion detection system using a filter-based feature selection algorithm

Mohammed A. Ambusaidi, Xiangjian He, Priyadarsi Nanda, Zhiyuan Tan

Research output: Journal PublicationArticlepeer-review

352 Citations (Scopus)

Abstract

Redundant and irrelevant features in data have caused a long-term problem in network traffic classification. These features not only slow down the process of classification but also prevent a classifier from making accurate decisions, especially when coping with big data. In this paper, we propose a mutual information based algorithm that analytically selects the optimal feature for classification. This mutual information based feature selection algorithm can handle linearly and nonlinearly dependent data features. Its effectiveness is evaluated in the cases of network intrusion detection. An Intrusion Detection System (IDS), named Least Square Support Vector Machine based IDS (LSSVM-IDS), is built using the features selected by our proposed feature selection algorithm. The performance of LSSVM-IDS is evaluated using three intrusion detection evaluation datasets, namely KDD Cup 99, NSL-KDD and Kyoto 2006+ dataset. The evaluation results show that our feature selection algorithm contributes more critical features for LSSVM-IDS to achieve better accuracy and lower computational cost compared with the state-of-the-art methods.

Original languageEnglish
Article number7387736
Pages (from-to)2986-2998
Number of pages13
JournalIEEE Transactions on Computers
Volume65
Issue number10
DOIs
Publication statusPublished - 1 Oct 2016
Externally publishedYes

Keywords

  • Intrusion detection
  • feature selection
  • least square support vector machine
  • linear correlation coefficient
  • mutual information

ASJC Scopus subject areas

  • Software
  • Theoretical Computer Science
  • Hardware and Architecture
  • Computational Theory and Mathematics

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