Simplified review on cyber security threats detection in IoT environment using deep learning approach

Abstract

Wearable technology, sensor networks, and home utilities are just a few of the businesses where the Internet of Things (IoT) is spreading quickly. With the development of the IoT, billions of gadgets are now connected to the internet and exchanging data. The proliferation of IoT devices has increased the number of IoT-based cyberattacks. In 2016 a massive denial of service (DDOS) cyber-attack was lunched utilizing infected internet of things devices a major website including Netflix and CNN was shutdown. Therefore, new ways for recognizing threats posed by hacked IoT nodes must be developed to overcome this concern. In that same context, ML and DL approaches are the best appropriate investigative control solution against IoT device-based intrusions. The point of the study is to offer a complete grasp of the IoT system-relevant technologies, standards, architecture, and the increasing dangers from corrupted IoT gadgets and an introduction to intrusion detection systems. Additionally, this research focuses on deep learning-based solutions for identifying IoT devices susceptible to cyber-attacks. The detection rate provided by deep learning algorithms shows promising results which reached 99% detection accuracy in some cases.