Unsupervised Anomaly Based Botnet Detection in IoT Networks

Sven Nomm, Hayretdin Bahsi

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

94 Scopus citations

Abstract

Anomaly-based detection of the IoT botnets with emphasis on feature selection is elaborated in this paper. Due to the rapid growth of the Internet of Things technology, the number of vulnerable devices that become a part of a botnet has grown significantly. The detection of such malicious traffic is essential for taking timely countermeasures. While the idea of anomaly-based attack detection is not new and has been extensively studied, much less attention has been paid to dimensionality reduction in learning models induced for IoT networks. In this paper, we showed that it is possible to induce high accurate unsupervised learning models with reduced feature set sizes, which enables to decrease the required computational resources. Training one common model for all IoT devices, instead of dedicated model for each device, is another design option that is evaluated for resource optimization.

Original languageEnglish (US)
Title of host publicationProceedings - 17th IEEE International Conference on Machine Learning and Applications, ICMLA 2018
EditorsM. Arif Wani, Mehmed Kantardzic, Moamar Sayed-Mouchaweh, Joao Gama, Edwin Lughofer
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1048-1053
Number of pages6
ISBN (Electronic)9781538668047
DOIs
StatePublished - Jul 2 2018
Externally publishedYes
Event17th IEEE International Conference on Machine Learning and Applications, ICMLA 2018 - Orlando, United States
Duration: Dec 17 2018Dec 20 2018

Publication series

NameProceedings - 17th IEEE International Conference on Machine Learning and Applications, ICMLA 2018

Conference

Conference17th IEEE International Conference on Machine Learning and Applications, ICMLA 2018
Country/TerritoryUnited States
CityOrlando
Period12/17/1812/20/18

Keywords

  • Anomaly detection
  • Botnet attack
  • Dimensionality reduction
  • Feature selection
  • Internet of things

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Science Applications
  • Computer Vision and Pattern Recognition
  • Safety, Risk, Reliability and Quality
  • Signal Processing
  • Decision Sciences (miscellaneous)

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