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Open AccessArticle

An Experimental Analysis of Attack Classification Using Machine Learning in IoT Networks

1
School of Computing, Edinburgh Napier University, Edinburgh EH10 5DT, UK
2
School of Electronics, Electrical Engineering and Computer Science, Queen’s University, Belfast BT9 5BN, UK
3
Department of Computer Science, Namal Institute, Mianwali 42250, Pakistan
4
College of Information Engineering, Yangzhou University, Yangzhou 225127, China
5
Department of Computer Science, King Fahad Naval Academy, Al Jubail 35512, Saudi Arabia
6
School of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081, China
*
Author to whom correspondence should be addressed.
Sensors 2021, 21(2), 446; https://doi.org/10.3390/s21020446
Received: 12 December 2020 / Revised: 6 January 2021 / Accepted: 7 January 2021 / Published: 10 January 2021
(This article belongs to the Special Issue AI for IoT)
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. View Full-Text
Keywords: Internet of Things (IoT); IoT attacks; security; intrusion detection systems; privacy; machine learning; ML models; multi-class classification Internet of Things (IoT); IoT attacks; security; intrusion detection systems; privacy; machine learning; ML models; multi-class classification
MDPI and ACS Style

Churcher, A.; Ullah, R.; Ahmad, J.; ur Rehman, S.; Masood, F.; Gogate, M.; Alqahtani, F.; Nour, B.; Buchanan, W.J. An Experimental Analysis of Attack Classification Using Machine Learning in IoT Networks. Sensors 2021, 21, 446.

AMA Style

Churcher A, Ullah R, Ahmad J, ur Rehman S, Masood F, Gogate M, Alqahtani F, Nour B, Buchanan WJ. An Experimental Analysis of Attack Classification Using Machine Learning in IoT Networks. Sensors. 2021; 21(2):446.

Chicago/Turabian Style

Churcher, Andrew; Ullah, Rehmat; Ahmad, Jawad; ur Rehman, Sadaqat; Masood, Fawad; Gogate, Mandar; Alqahtani, Fehaid; Nour, Boubakr; Buchanan, William J. 2021. "An Experimental Analysis of Attack Classification Using Machine Learning in IoT Networks" Sensors 21, no. 2: 446.

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Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

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