  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.
