Abstract
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 anintrusion detection system to detect and classify attacks in an appropriate time and automated manner increasesparticularly because of the use of IoT and the nature of its data that causes an increase in attacks. Malicious attacksare continuously changing, causing new attacks. In this study, we present a survey about the detection of anomaliesand detect intrusion by distinguishing between normal and malicious behaviors whilst analyzing network traffic todiscover new attacks. This study surveys previous research by evaluating their performance through two categoriesof new datasets of real traffic (i.e. CSE-CIC-IDS2018 and Bot-IoT datasets). To evaluate the performance, we showaccuracy measurement for detect intrusion in different systems
Recommended Citation
Farhan, Baraa I. and Jasim, Ammar D.
(2022)
"Survey of Intrusion Detection Using Deep Learning in the Internetof Things,"
Iraqi Journal for Computer Science and Mathematics: Vol. 3:
Iss.
1, Article 9.
DOI: https://doi.org/10.52866/ijcsm.2022.01.01.009
Available at:
https://ijcsm.researchcommons.org/ijcsm/vol3/iss1/9
