Investigation of Machine Learning-based Software Definition Network for Intrusion Detection in IoT

Abstract

The World Wide Web of Things will impact many aspects of our lives. Home automation gadgets, sensors, and garments use it. Internet of Things devices shine out for connectivity, wide use, and cheap computing power. By 2024, 50 billion items will be connected to the Web as gadgets for the Internet of Things develop increasingly prevalent. IoT-enabled software-defined networks manage large amounts of unpredictable internet traffic. However, huge internet traffic makes it hard to identify fraudulent activity. IoT device safety investigations are best done with machine learning and deep learning. SDN-based IoT vulnerability protocols, architecture, and dangers are the focus of this study. Intrusion detection methods are listed below. The investigation also examines machine learning and deep learning strategies for detecting Internet of Things gadgets at risk of infiltration.