IoT security with Deep Learning-based Intrusion Detection Systems: A systematic literature review
Idriss Idrissi, Mostafa Azizi, Omar Moussaoui
Abstract
In the recent years, Internet of things (IoT) is rising increasingly to become a big research topic due to the billions of devices dispatched around the world. These devices are connected to the Internet and communicate directly with each other without human intervention. However, this creates new security challenges, which are increasing more and more and becoming relevant research issues. Our study in this paper focuses on the state-of-the-art of IoT security threats and vulnerabilities by conducting a classification of some wellknown security threats according to Cisco IoT reference model architecture. We also make a review of existing works in the area of IoT security targeting more particularly the Intrusion Detection Systems based on Deep Learning (DL) techniques, which are rising as emerging techniques in various fields including cybersecurity. This state-of-the-art and its findings can serve as a potential basis for future research directions.