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

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

研究成果: Conference contribution

7 被引用数 (Scopus)

抄録

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.

本文言語English
ホスト出版物のタイトルProceedings - International Electronics Symposium on Knowledge Creation and Intelligent Computing, IES-KCIC 2017
出版社Institute of Electrical and Electronics Engineers Inc.
ページ46-49
ページ数4
2017-January
ISBN(電子版)9781538607169
DOI
出版ステータスPublished - 2017 12 19
イベント6th International Electronics Symposium on Knowledge Creation and Intelligent Computing, IES-KCIC 2017 - Surabaya, Indonesia
継続期間: 2017 9 262017 9 27

Other

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

ASJC Scopus subject areas

  • Artificial Intelligence
  • Signal Processing

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