A Modern Analysis of Aging Machine Learning Based IoT Cybersecurity Methods

Sam Strecker¹, Rushit Dave^1, , Nyle Siddiqui¹ and Naeem Seliya¹

¹Department of Computer Science, University of Wisconsin – Eau Claire, Eau Claire, US

Cite this paper:
Sam Strecker, Rushit Dave, Nyle Siddiqui and Naeem Seliya. A Modern Analysis of Aging Machine Learning Based IoT Cybersecurity Methods. Journal of Computer Sciences and Applications. 2021; 9(1):16-22. doi: 10.12691/jcsa-9-1-2

Abstract

Modern scientific advancements often contribute to the introduction and refinement of never-before-seen technologies. This can be quite the task for humans to maintain and monitor and as a result, our society has become reliant on machine learning to assist in this task. With new technology comes new methods and thus new ways to circumvent existing cyber security measures. This study examines the effectiveness of three distinct Internet of Things cyber security algorithms currently used in industry today for malware and intrusion detection: Random Forest (RF), Support-Vector Machine (SVM), and K-Nearest Neighbor (KNN). Each algorithm was trained and tested on the Aposemat IoT-23 dataset which was published in January 2020 with the earliest of captures from 2018 and latest from 2019. The RF, SVM, and KNN reached peak accuracies of 92.96%, 86.23%, and 91.48%, respectively, in intrusion detection and 92.27%, 83.52%, and 89.80% in malware detection. It was found all three algorithms are capable of being effectively utilized for the current landscape of IoT cyber security in 2021.

Keywords:
internet of things machine learning cybersecurity intrusion detection malware detection botnet attacks

This work is licensed under a Creative Commons Attribution 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

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