Detection of IPv6 routing attacks using ANN and a novel IoT dataset

Abstract

The Internet of Things (IoT) is an intelligent network paradigm created by interconnected device networks. Although the importance of IoT systems has increased in various applications, the increasing number of connected devices has made security even more critical. This study presents the ROUT-4-2023 dataset, which represents a step toward the security of IoT networks. This dataset simulates potential attacks on RPL-based IoT networks and provides a new platform for researchers in this field. Using artificial intelligence and machine-learning techniques, a performance evaluation was performed on four different artificial neural network models (convolutional neural network, deep neural network, multilayer perceptron structure, and routing attack detection-fed forward neural network [RaD-FFNN]). The results show that the RaD-FFNN model has high accuracy, precision, and retrieval rates, indicating that it can be used as an effective tool for the security of IoT networks. This study contributes to the protection of IoT networks from potential attacks by presenting ROUT-4-2023 and RaD-FFNN models, which will lead to further research on IoT security.