Security of Internet Of Things Using Machine Learning

Abstract

In the last few years, the Internet Of Things (IoT) technology has become more and more used and widespread in different aspects of daily life thanks to intelligent services offered in several fields (robotics, home, hospitals, etc). IoT systems are vulnerable to a variety of security attacks, and they are facing multiple risks and considered targets for cyberattacks, for this reason, securing IoT system is a major challenge, existing security protocols based on tradition are not suitable for them. To cope with different security challenges, Machine Learning (ML) Techniques are able to provide intelligence for IoT devices and networks. This paper presents the security problems and the existing ML solutions to manage security aspects related to the IoT domain. This paper proposes a classification model to detect attack on the UNSW-NB18 dataset and implements the following algorithms namely, Decision Tree (DT), Random Forest (RF), Logistic Regression (LR) and k-Nearest Neighbors (KNN). The best results were achieved by the Random Forest algorithm, with an accuracy of 99.96%.