RPL Attacks Detection and Prevention in IOT Networks with Advanced GRU Deep Learning Algorithm

Abstract

Cyber-attacks on the Internet of Things have improved significantly more in previous years, with the expansion of intelligent internet-connected systems and functions. The Internet of Things aims to create a better environment for people to automatically understand their needs and act accordingly. This project aims to identify a wormhole attack against Routing Protocol for Low Power and Lossy Networks (RPL) of the Internet of Things, which is the technology behind most of the devices and sensors in the Internet of Things. This study proposes a deep learning-based advanced gated recurrent unit (AGRU) network model. The proposed model is compared to Logistic regression and One Support Vector Machine using different weight states and node power consumption. As a result, the model’s predictions and promises regarding IoT security and source effectiveness seem to be accurate. In terms of source efficiency and IoT security, it is evident that the results confirmed the commitment and expectations of the study. According to previous literature studies, RPL flood attacks are associated with a reduced error rate in detecting attacks.