IoT Intrusion Detection System based on Machine Learning Algorithms using the UNSW-NB15 dataset

Abstract

The evolution of communications systems with the advent of IoT is leading to an increase in attacks against them. This is due to the fact that the security of connected objects in the IoT is an emerging area still which requires preventive solutions against various attacks. At the network security level, Intrusion Detection Systems (IDS) are used to analyze network data and detect abnormal behavior in the network. In this work, we implemented different machine learning models to build an intrusion detection system based on the UNSW NB15 dataset. To do this, we did data cleaning and feature engineering on the data in the pre-processing phase. Then we used various models such as logistic regression, SVM classifier, decision tree, random forest, XGBoost in order to predict attacks. Finally, an intrusion detection system is trained on various machine learning algorithms and we selected the most effective model. Experiments were carried out on the UNSW-NB15 dataset and subsequently we compared other machine learning algorithms, and it results that the random forest model on important parameters has a clear advantage in the detection of rare abnormal behaviors.