An intelligent algorithm with feature selection and decision rules applied to anomaly intrusion detection

Shih Wei Lin, Kuo Ching Ying, Chou Yuan Lee, Zne Jung Lee*

*Corresponding author for this work

Research output: Contribution to journalJournal Article peer-review

175 Scopus citations

Abstract

Intrusion detection system (IDS) is to monitor the attacks occurring in the computer or networks. Anomaly intrusion detection plays an important role in IDS to detect new attacks by detecting any deviation from the normal profile. In this paper, an intelligent algorithm with feature selection and decision rules applied to anomaly intrusion detection is proposed. The key idea is to take the advantage of support vector machine (SVM), decision tree (DT), and simulated annealing (SA). In the proposed algorithm, SVM and SA can find the best selected features to elevate the accuracy of anomaly intrusion detection. By analyzing the information from using KDD'99 dataset, DT and SA can obtain decision rules for new attacks and can improve accuracy of classification. In addition, the best parameter settings for the DT and SVM are automatically adjusted by SA. The proposed algorithm outperforms other existing approaches. Simulation results demonstrate that the proposed algorithm is successful in detecting anomaly intrusion detection.

Original languageEnglish
Pages (from-to)3285-3290
Number of pages6
JournalApplied Soft Computing Journal
Volume12
Issue number10
DOIs
StatePublished - 10 2012
Externally publishedYes

Keywords

  • Anomaly detection
  • Decision tree
  • Intelligent algorithm
  • Simulated annealing
  • Support vector machine

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