A multi-measure feature selection algorithm for efficacious intrusion detection

Vitali Herrera-Semenets*, Lázaro Bustio-Martínez, Raudel Hernández-León, Jan van den Berg

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

2 Citations (Scopus)
4 Downloads (Pure)

Abstract

Every day the number of devices interacting through telecommunications networks grows resulting into an increase in the volume of data and information generated. At the same time, a growing number of information security incidents is being observed including the occurrence of unauthorized accesses, also named intrusions. As a consequence of these two developments, Information and Communications services providers require automated processes to detect and solve such intrusions, and this should done quickly in order to keep the related cybersecurity risks at acceptable levels. However, the presence of large volumes of data negatively interferes with the performance of classifiers used in intrusion detection tasks, which limits their applicability in practical cases. The research reported in this paper focuses on proposing a novel feature selection algorithm for intrusion detection scenarios. To this end, an extensive literature review was executed to first discover issues in the feature selection algorithms reported. Based on the insights obtained, the new multi-measure feature selection algorithm was designed that uses qualitative information provided by multiple feature selection measures, and reduces the dimensionality of the training data set. The algorithm proposed was next extensively tested using various data sets. It provides greater efficacy than other feature selection algorithms used for intrusion detection purposes. We finalize by providing some ideas on future research in order to further improve the algorithm.

Original languageEnglish
Article number107264
Number of pages11
JournalKnowledge-Based Systems
Volume227
DOIs
Publication statusPublished - 2021

Bibliographical note

Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care
Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Keywords

  • Data reduction
  • Feature selection
  • Intrusion detection algorithms

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