2 Citations (Scopus)

Abstract

Intrusion Detection System (IDS) is a scheme, which supervises network traffic and monitors suspicious activities in a network system. Nowadays, a potential solution to efficiently detect network intrusions is to use a machine learning (ML)based IDS system. There are numerous issues with IDS, mainly in the dataset for the training. One of the problems that often arises is increasing detection accuracy and minimizing computation time in training the data. There is a suitable dataset for detecting various intrusions, which is the NSL-KDD. In this dataset, there is a number of features that are redundant and irrelevant to access. We suggest a strategy in this study to increase IDS performance by combining univariate selection and Support Vector Machine (SVM) for classification. It is ideal for categorization in IDS because it has high performance. Data reduction is used to increase the accuracy and decrease computation time. The result of experiments shows that the proposed method effectively improves the accuracy.

Original languageEnglish
Title of host publicationICOIACT 2021 - 4th International Conference on Information and Communications Technology
Subtitle of host publicationThe Role of AI in Health and Social Revolution in Turbulence Era
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages164-168
Number of pages5
ISBN (Electronic)9781665433945
DOIs
Publication statusPublished - 2021
Event4th International Conference on Information and Communications Technology, ICOIACT 2021 - Virtual, Online, Indonesia
Duration: 30 Aug 2021 → …

Publication series

NameICOIACT 2021 - 4th International Conference on Information and Communications Technology: The Role of AI in Health and Social Revolution in Turbulence Era

Conference

Conference4th International Conference on Information and Communications Technology, ICOIACT 2021
Country/TerritoryIndonesia
CityVirtual, Online
Period30/08/21 → …

Keywords

  • Data reduction
  • Intrusion detection system
  • Local outlier factor
  • Network infrastructure
  • Univariate selection

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