Pearson Correlation Attribute Evaluation-based Feature Selection for Intrusion Detection System

Yuna Sugianela, Tohari Ahmad

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

11 Citations (Scopus)

Abstract

IDS helps to overcome the network attack by taking appropriate preventive measures. The data mining method has good adaptability to new attack types; however, it consumes much time for high dimensional data. Therefore, the system needs a reduction of that high dimension. In this paper, we use a correlation approach of the attribute to evaluate those high dimensional data. To achieve a better environment, we propose a cut-off value of correlation to select some best features to use in the classification process. The best cut-off value in our experiment is 0.2 in RF classification that reaches 99.36% accuracy. The selection feature can reduce the time consumed in the running system.

Original languageEnglish
Title of host publicationProceeding - ICoSTA 2020
Subtitle of host publication2020 International Conference on Smart Technology and Applications: Empowering Industrial IoT by Implementing Green Technology for Sustainable Development
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728130835
DOIs
Publication statusPublished - Feb 2020
Event2020 International Conference on Smart Technology and Applications, ICoSTA 2020 - Surabaya, Indonesia
Duration: 20 Feb 2020 → …

Publication series

NameProceeding - ICoSTA 2020: 2020 International Conference on Smart Technology and Applications: Empowering Industrial IoT by Implementing Green Technology for Sustainable Development

Conference

Conference2020 International Conference on Smart Technology and Applications, ICoSTA 2020
Country/TerritoryIndonesia
CitySurabaya
Period20/02/20 → …

Keywords

  • Pearson's correlation
  • feature
  • intrusion detection system
  • network security
  • selection

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