Trust Based Detection Of Rank Attack In Internet Of Things

Abstract

The Internet of Things (IoT) transforms everyday objects into smarter devices using technologies such as sensor networks.The routing Protocol for Lossy and Low-Power Networks (RPL) is a key protocol suitable for IoT environments, which often consists of devices with limited processing power, memory, and networking capabilities.Recent research has focused on developing IDS approaches that are more suitable for the IoT.In recent studies, machine learning has been used for feature selection in the IoT dataset, which helps to reduce the dimensions of the features and improve the detection accuracy.In some studies, deep learning methods have been used to handle large amounts of data and provide real-time analyses.Transfer learning has also evolved to adapt to IoT threats.This paper presents a novel approach for identifying intrusions in IoT networks.A hybrid approach using deep learning methods that combine trust-based parameters to enhance the early detection of known and unknown attacks in IoT traffic.