DDoS Attack Detection on IoT Devices Using Machine Learning Techniques

Abstract

The Internet of Things made life easy and simple. It has become a part of life to control the devices and activate them for various applications using the Internet. Due to the drastic increase of its usage in day-to-day life, researchers are moving towards the concept of everything connected to the internet which can lead to penetration. To avoid malicious penetrations into the network, it is required to develop a reliable mechanism for secure communication over IoT devices. In order to find the best accurate algorithm, many Machine Learning (ML), as well as Deep Learning (DL) methods, need to be applied to the collected dataset for the detection of DDoS attacks. Hence in this paper, an effective model is selected by applying and comparing all the ML and DL models on a dataset. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.