Feature selection using genetic algorithm to improve classification in network intrusion detection system

Andrey Ferriyan, Achmad Husni Thamrin, Keiji Takeda, Jun Murai

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)

Abstract

In this paper, we present Genetic Algorithm based optimized feature selections for intrusion detection systems. We used one-point crossover for the Genetic Algorithm parameters instead of two-point crossover used by the previous research as it one-point crossover is faster. For evaluations, we used the NSL-KDD Cup 99 data set and we modified the data set by looking into to the recent attacks, hence making the data set more relevant to the current situations. Several classifiers were used on these data sets and we found that Random Forest gave the best results in terms of the classification rate and the training time. The results also showed that our parameters performed better in these two metrics and the classifications using our optimized features on the modified data sets gave mixed results compared to ones with the original features.

Original languageEnglish
Title of host publicationProceedings - International Electronics Symposium on Knowledge Creation and Intelligent Computing, IES-KCIC 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages46-49
Number of pages4
Volume2017-January
ISBN (Electronic)9781538607169
DOIs
Publication statusPublished - 2017 Dec 19
Event6th International Electronics Symposium on Knowledge Creation and Intelligent Computing, IES-KCIC 2017 - Surabaya, Indonesia
Duration: 2017 Sep 262017 Sep 27

Other

Other6th International Electronics Symposium on Knowledge Creation and Intelligent Computing, IES-KCIC 2017
CountryIndonesia
CitySurabaya
Period17/9/2617/9/27

Keywords

  • feature selection
  • genetic algorithm
  • intrusion detection
  • random forest

ASJC Scopus subject areas

  • Artificial Intelligence
  • Signal Processing

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  • Cite this

    Ferriyan, A., Thamrin, A. H., Takeda, K., & Murai, J. (2017). Feature selection using genetic algorithm to improve classification in network intrusion detection system. In Proceedings - International Electronics Symposium on Knowledge Creation and Intelligent Computing, IES-KCIC 2017 (Vol. 2017-January, pp. 46-49). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/KCIC.2017.8228458