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. 2021 Jan 10;21(2):446.
doi: 10.3390/s21020446.

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

Andrew Churcher  1 , Rehmat Ullah  2 , Jawad Ahmad  1 , Sadaqat Ur Rehman  3 , Fawad Masood  4 , Mandar Gogate  1 , Fehaid Alqahtani  5 , Boubakr Nour  6 , William J Buchanan  1
Affiliations

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

Andrew Churcher et al. Sensors (Basel). .

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.

Keywords: Internet of Things (IoT); IoT attacks; ML models; intrusion detection systems; machine learning; multi-class classification; privacy; security.

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Conflict of interest statement

The authors declare no conflict of interest.

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References

    1. Dorsemaine B., Gaulier J.P., Wary J.P., Kheir N., Urien P. Internet of Things: A Definition & Taxonomy; Proceedings of the 2015 9th International Conference on Next Generation Mobile Applications, Services and Technologies; Cambridge, UK. 9–11 September 2015; - DOI
    1. Statista . IoT: Number of Connected Devices Worldwide 2012–2025. Statista; Hamburg, Germany: 2019.
    1. Doffman Z. Cyberattacks On IOT Devices Surge 300% In 2019, ‘Measured in Billions’. [(accessed on 10 November 2020)]; Available online: https://www.forbes.com/sites/zakdoffman/2019/09/14/dangerous-cyberattack....
    1. Furbush J. Machine Learning: A Quick and Simple Definition. [(accessed on 10 November 2020)]; Available online: https://www.oreilly.com/content/machine-learning-a-quick-and-simple-defi...
    1. Jmj A. 5 Industries That Heavily Rely on Artificial Intelligence and Machine Learning. [(accessed on 10 November 2020)]; Available online: https://medium.com/datadriveninvestor/5-industries-that-heavily-rely-on-....

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