Hybrid feature selection models for machine learning based botnet detection in IoT networks

Alejandro Guerra-Manzanares, Hayretdin Bahsi, Sven Nomm

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

41 Citations (Scopus)

Abstract

Timely detection of intrusions is essential in IoT networks, considering the massive attacks launched by the huge-sized botnets which are composed of insecure devices. Machine learning methods have demonstrated promising results for the detection of such attacks. However, the effectiveness of such methods may greatly benefit from the reduction of feature set size as this may prevent the impeding impact of unnecessary features and minimize the computational resources required for intrusion detection in such networks having several limitations. This paper elaborates on feature selection methods applied to machine learning models which are induced for botnet detection in IoT networks. A particular attention is devoted to the use of wrapper methods and their combination with filter methods. While filter-based feature selection methods provide a computationally light approach to select the most informative features, it is shown that their utilization in combination with wrapper methods boosts up the detection accuracy.

Original languageEnglish
Title of host publicationProceedings - 2019 International Conference on Cyberworlds, CW 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages324-327
Number of pages4
ISBN (Electronic)9781728122977
DOIs
Publication statusPublished - Oct 2019
Externally publishedYes
Event18th International Conference on Cyberworlds, CW 2019 - Kyoto, Japan
Duration: 2 Oct 20194 Oct 2019

Publication series

NameProceedings - 2019 International Conference on Cyberworlds, CW 2019

Conference

Conference18th International Conference on Cyberworlds, CW 2019
Country/TerritoryJapan
CityKyoto
Period2/10/194/10/19

Keywords

  • Botnet
  • Feature selection
  • Internet of Things
  • Machine learning
  • Wrapper method

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computational Theory and Mathematics
  • Computer Graphics and Computer-Aided Design
  • Computer Science Applications
  • Computer Vision and Pattern Recognition
  • Hardware and Architecture
  • Media Technology

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