Dimensionality Reduction for Machine Learning Based IoT Botnet Detection

Hayretdin Bahsi, Sven Nomm, Fabio Benedetto La Torre

Research output: Chapter in Book/Report/Conference proceedingConference contribution

88 Scopus citations

Abstract

The rapid development of the internet of things caused severe security problems such as the cyber attacks launched by extremely huge botnets comprised of IoT devices. The detection of these devices is essential for protecting the networks. Recently, some of the studies have demonstrated the high accuracy of machine learning methods, including deep learning, in detecting IoT botnets. However, the minimizing of the required features for classification is highly needed for overcoming scalability and computation resource problems in IoT environments. Having results which can be readily interpretable by cyber security analysts and producing signatures for the contemporary intrusion detection or network monitoring systems are other significant factors in this area in which quick and widespread security adaption is highly required. In this study, we applied feature selection to minimize the number of features in detecting the IoT bots. It is shown that fewer features can achieve very high accuracy rates and afford interpretable results with a multi-class classifier based on a shallow method, decision tree.

Original languageEnglish (US)
Title of host publication2018 15th International Conference on Control, Automation, Robotics and Vision, ICARCV 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1857-1862
Number of pages6
ISBN (Electronic)9781538695821
DOIs
StatePublished - Dec 18 2018
Externally publishedYes
Event15th International Conference on Control, Automation, Robotics and Vision, ICARCV 2018 - Singapore, Singapore
Duration: Nov 18 2018Nov 21 2018

Publication series

Name2018 15th International Conference on Control, Automation, Robotics and Vision, ICARCV 2018

Conference

Conference15th International Conference on Control, Automation, Robotics and Vision, ICARCV 2018
Country/TerritorySingapore
CitySingapore
Period11/18/1811/21/18

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Control and Optimization

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