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

TLDR

The rapid growth of IoT devices, coupled with their limited resources and often neglected security, has made them attractive targets and overwhelmed traditional IDS. The study aims to evaluate a range of machine‑learning techniques—KNN, SVM, DT, NB, RF, ANN, and LR—for intrusion detection in IoT networks. Using the Bot‑IoT dataset, the authors compared these algorithms for binary and multi‑class attack classification, assessing accuracy, precision, recall, F1 score, and log loss. Random forest achieved 99 % accuracy on HTTP DDoS and outperformed other methods in binary classification, while k‑nearest neighbours reached 99 % accuracy—4 % higher than RF—in multi‑class classification.

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.