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IJMLC 2021 Vol.11(6): 399-406 ISSN: 2010-3700
DOI: 10.18178/ijmlc.2021.11.6.1068

Machine Learning Based Intrusion Detection for IoT Botnet

Sikha Bagui, Xiaojian Wang, and Subhash Bagui

Abstract—In this article, we analyzed botnet traffic in an IoT environment using three machine learning classifiers: Logistic Regression, Support-Vector Machine and Random Forest. We classified each attack in each botnet for nine devices. We calculated the Accuracy, True Positive, False Positive, False Negative, True Negative, Precision, Recall, F1 score for each algorithm. We obtained impressive results (above 99%) using these three classifiers. We have a high attack detection rate. A brief analysis of the results is presented.

Index Terms—Intrusion detection, machine learning, internet of things (IoT), botnet, logistic regression (LR), support vector machines (SVM), random forest (RF).

Sikha Bagui and Xiaojian Wang are with the Department of Computer Science, University of West Florida, Pensacola, FL 32514 USA (e-mail: bagui@uwf.edu; xw5@students.uwf.edu).
Subhash Bagui is with the Department of Mathematics and Statistics, University of West Florida, Pensacola, FL 32514 USA (e-mail: sbagui@uwf.edu).

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Cite: Sikha Bagui, Xiaojian Wang, and Subhash Bagui, "Machine Learning Based Intrusion Detection for IoT Botnet," International Journal of Machine Learning and Computing vol. 11, no. 6, pp. 399-406, 2021.

Copyright © 2021 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

 

General Information

  • ISSN: 2010-3700 (Online)
  • Abbreviated Title: Int. J. Mach. Learn. Comput.
  • Frequency: Bimonthly
  • DOI: 10.18178/IJMLC
  • Editor-in-Chief: Dr. Lin Huang
  • Executive Editor:  Ms. Cherry L. Chen
  • Abstracing/Indexing: Inspec (IET), Google Scholar, Crossref, ProQuest, Electronic Journals LibraryCNKI.
  • E-mail: ijmlc@ejournal.net


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