Denial of Service Detection for IoT Networks Using Machine Learning

Abstract

The Internet of Things (IoT) is considered one of the trending technologies today. IoT affects a variety of industries, including logistics tracking, healthcare, automotive and smart cities. A rising number of cyberattacks and breaches are rapidly targeting networks equipped with IoT devices. Due to the resource-constrained nature of the IoT devices, one of the Internet security issues impacting IoT devices is the Denial-of-Service (DoS). This encourages the development of new techniques for automatically detecting DoS in IoT networks. In this paper, we test the performance of the following Machine Learning (ML) algorithms in detecting IoT DoS attacks using packet analysis at regular time intervals: Neural Networks (NN), Gaussian Naive Bayes (NB), Decision Trees (DT), and Support Vector Machine (SVM). We were able to achieve 98% accuracy in intrusion detection for IoT devices. We have created a novel way of detecting the attacks using only six attributes, which significantly reduces the time to train the ML Models by 58% on average. This research is based on data collected from actual IoT attacks on IoT networks. This paper shows that using the DT or NN; we can detect attacks on IoT devices. Furthermore, it shows that NB and SVM are poor in detecting IoT attacks. In addition, it proves that middle boxes embedded with ML Models can be utilized to detect attacks in places such as houses, manufactures, and plants.