Andrew Churcher
An experimental analysis of attack classification using machine learning in IoT networks
Churcher, Andrew; Ullah, Rehmat; Ahmad, Jawad; Ur Rehman, Sadaqat; Masood, Fawad; Gogate, Mandar; Alqahtani, Fehaid; Nour, Boubakr; Buchanan, William J
Authors
Rehmat Ullah
Dr Jawad Ahmad J.Ahmad@napier.ac.uk
Lecturer
Sadaqat Ur Rehman
Fawad Masood
Dr. Mandar Gogate M.Gogate@napier.ac.uk
Senior Research Fellow
Fehaid Alqahtani
Boubakr Nour
Prof Bill Buchanan B.Buchanan@napier.ac.uk
Professor
Abstract
In recent years, there has been a massive increase in the amount of Internet of Things (IoT) devices as well as the data generated by such devices. The participating devices in IoT networks can be problematic due to their resource-constrained nature, and integrating security on these devices is often overlooked. This has resulted in attackers having an increased incentive to target IoT devices. As the number of attacks possible on a network increases, it becomes more difficult for traditional intrusion detection systems (IDS) to cope with these attacks efficiently. In this paper, we highlight several machine learning (ML) methods such as k-nearest neighbour (KNN), support vector machine (SVM), decision tree (DT), naive Bayes (NB), random forest (RF), artificial neural network (ANN), and logistic regression (LR) that can be used in IDS. In this work, ML algorithms are compared for both binary and multi-class classification on Bot-IoT dataset. Based on several parameters such as accuracy, precision, recall, F1 score, and log loss, we experimentally compared the aforementioned ML algorithms. In the case of HTTP distributed denial-of-service (DDoS) attack, the accuracy of RF is 99%. Furthermore, other simulation results-based precision, recall, F1 score, and log loss metric reveal that RF outperforms on all types of attacks in binary classification. However, in multi-class classification, KNN outperforms other ML algorithms with an accuracy of 99%, which is 4% higher than RF.
Journal Article Type | Article |
---|---|
Acceptance Date | Jan 7, 2021 |
Online Publication Date | Jan 10, 2021 |
Publication Date | 2021 |
Deposit Date | Jan 10, 2021 |
Publicly Available Date | Jan 11, 2021 |
Journal | Sensors |
Electronic ISSN | 1424-8220 |
Publisher | MDPI |
Peer Reviewed | Peer Reviewed |
Volume | 21 |
Issue | 2 |
Article Number | 446 |
DOI | https://doi.org/10.3390/s21020446 |
Keywords | Internet of Things (IoT); IoT attacks; security; intrusion detection systems; privacy; machine learning; ML models; multi-class classification |
Public URL | http://researchrepository.napier.ac.uk/Output/2714228 |
Publisher URL | https://www.mdpi.com/1424-8220/21/2/446 |
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Copyright Statement
Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/
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