Botnet Detection in IoT Devices Using Random Forest Classifier with Independent Component Analysis

Authors

  • Nazmus Sakib Akash Department of Computing & Information System, Daffodil International University, Bangladesh
  • Shakir Rouf Department of Computer Science & Engineering, BRAC University, Bangladesh
  • Sigma Jahan Faculty of Computer Science, Dalhousie University, Canada
  • Amlan Chowdhury Department of Computer Science & Engineering, BRAC University, Bangladesh
  • Jia Uddin AI and Big Data Department, Endicott College, Woosong University, South Korea

DOI:

https://doi.org/10.32890/jict2022.21.2.3

Abstract

With rapid technological progress in the Internet of Things (IoT), it has become imperative to concentrate on its security aspect. This paper represents a model that accounts for the detection of botnets through the use of machine learning algorithms. The model examined anomalies, commonly referred to as botnets, in a cluster of IoT devices attempting to connect to a network. Essentially, this paper exhibited the use of transport layer data (User Datagram Protocol - UDP) generated through IoT devices. An intelligent novel model comprising Random Forest Classifier with Independent Component Analysis (ICA) was proposed for botnet detection in IoT devices. Various machine learning algorithms were also implemented upon the processed data for comparative analysis. The experimental results of the proposed model generated state-of-the-art results for three different datasets, achieving up to 99.99% accuracy effectively with the lowest prediction time of 0.12 seconds without overfitting. The significance of this study lies in detecting botnets in IoT devices effectively and efficiently under all circumstances by utilizing ICA with Random Forest Classifier, which is a simple machine learning algorithm.

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Published

07-04-2022

How to Cite

Akash, N. S. ., Rouf, S. ., Jahan, S. ., Chowdhury, A. ., & Uddin, J. . (2022). Botnet Detection in IoT Devices Using Random Forest Classifier with Independent Component Analysis. Journal of Information and Communication Technology, 21(2), 201–232. https://doi.org/10.32890/jict2022.21.2.3