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In recent years, there has been a massive increase in the amount of Internetof Things (IoT) devices as well as the data generated by such devices. Theparticipating devices in IoT networks can be problematic due to theirresource-constrained nature, and integrating security on these devices is oftenoverlooked. This has resulted in attackers having an increased incentive totarget IoT devices. As the number of attacks possible on a network increases,it becomes more difficult for traditional intrusion detection systems (IDS) tocope with these attacks efficiently. In this paper, we highlight severalmachine learning (ML) methods such as k-nearest neighbour (KNN), support vectormachine (SVM), decision tree (DT), naive Bayes (NB), random forest (RF),artificial neural network (ANN), and logistic regression (LR) that can be usedin IDS. In this work, ML algorithms are compared for both binary andmulti-class classification on Bot-IoT dataset. Based on several parameters suchas accuracy, precision, recall, F1 score, and log loss, we experimentallycompared the aforementioned ML algorithms. In the case of HTTP distributeddenial-of-service (DDoS) attack, the accuracy of RF is 99%. Furthermore, othersimulation results-based precision, recall, F1 score, and log loss metricreveal that RF outperforms on all types of attacks in binary classification.However, in multi-class classification, KNN outperforms other ML algorithmswith an accuracy of 99%, which is 4% higher than RF.

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