Survey of Intrusion Detection Using Deep Learning in the Internet of Things

The use of deep learning in various models is a powerful tool in detecting Internet of Things (IoT) attacks and identifying new types of intrusion to access a better secure network. The need to develop an intrusion detection system to detect and classify attacks in an appropriate time and automated manner increases particularly because of the use of IoT and the nature of its data that causes an increase in attacks. Malicious attacks are continuously changing, causing new attacks. In this study, we present a survey about the detection of anomalies and detect intrusion by distinguishing between normal and malicious behaviors whilst analyzing network traffic to discover new attacks. This study surveys previous research by evaluating their performance through two categories of new datasets of real traffic (i.e. CSE-CIC-IDS2018 and Bot-IoT datasets). To evaluate the performance, we show accuracy measurement for detect intrusion in different systems. © 2022 Iraqi Journal for Computer Science and Mathematics. All rights reserved.