Machine Learning Algorithms for Intrusion Detection in IoT Prediction and Performance Analysis

Abstract

The rapid growth of the Internet of Things (IoT) has created a large attack surface for adversaries to launch destructive cyber attacks. Intrusion Detection Systems (IDS) can be used to identify and monitor anomalies and threats in the IoT system. In this paper, we compare the performance of various machine learning algorithms for building IDS, including decision tree (DT), random forest (RF), k-Nearest Neighbor (k-NN), Ada Boost, and support vector machine (SVM), using the IoTID20 dataset. This dataset has three target classes, including a binary class for normal or abnormal behavior and classes for categories and sub-categories of the binary class. We select the most relevant features to minimize execution time and improve accuracy. Our results show that the decision tree algorithm generally achieved the highest accuracy at 99.80% with the lowest error rate compared to the other algorithms.