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

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

doi:10.1109/KCIC.2017.8228458

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

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

Faculty of Environment and Information Studies

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

7 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 language	English
Title of host publication	Proceedings - International Electronics Symposium on Knowledge Creation and Intelligent Computing, IES-KCIC 2017
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	46-49
Number of pages	4
Volume	2017-January
ISBN (Electronic)	9781538607169
DOIs	https://doi.org/10.1109/KCIC.2017.8228458
Publication status	Published - 2017 Dec 19
Event	6th International Electronics Symposium on Knowledge Creation and Intelligent Computing, IES-KCIC 2017 - Surabaya, Indonesia Duration: 2017 Sep 26 → 2017 Sep 27

Other

Other	6th International Electronics Symposium on Knowledge Creation and Intelligent Computing, IES-KCIC 2017
Country	Indonesia
City	Surabaya
Period	17/9/26 → 17/9/27

Keywords

feature selection
genetic algorithm
intrusion detection
random forest

ASJC Scopus subject areas

Artificial Intelligence
Signal Processing

Access to Document

10.1109/KCIC.2017.8228458

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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

Feature selection using genetic algorithm to improve classification in network intrusion detection system. / Ferriyan, Andrey; Thamrin, Achmad Husni; Takeda, Keiji ; Murai, Jun.

Proceedings - International Electronics Symposium on Knowledge Creation and Intelligent Computing, IES-KCIC 2017. Vol. 2017-January Institute of Electrical and Electronics Engineers Inc., 2017. p. 46-49.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Ferriyan, A, Thamrin, AH, 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, Institute of Electrical and Electronics Engineers Inc., pp. 46-49, 6th International Electronics Symposium on Knowledge Creation and Intelligent Computing, IES-KCIC 2017, Surabaya, Indonesia, 17/9/26. https://doi.org/10.1109/KCIC.2017.8228458

Ferriyan A, Thamrin AH, Takeda K , Murai J. 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. Institute of Electrical and Electronics Engineers Inc. 2017. p. 46-49 https://doi.org/10.1109/KCIC.2017.8228458

Ferriyan, Andrey ; Thamrin, Achmad Husni ; Takeda, Keiji ; Murai, Jun. / Feature selection using genetic algorithm to improve classification in network intrusion detection system. Proceedings - International Electronics Symposium on Knowledge Creation and Intelligent Computing, IES-KCIC 2017. Vol. 2017-January Institute of Electrical and Electronics Engineers Inc., 2017. pp. 46-49

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