RPL rank attack detection using Deep Learning
Wijdan Choukri, Hanane Lamaazi, Nabil Benamar
Abstract
Internet of Things (IoT) is a network of interconnected smart devices. It provides a set of services in different domains to improve the quality of human daily life. However, protecting information systems and transmitted data from attacks is critical in IoT especially for devices running over Low Power and Lossy Networks (LLNs) and using RPL routing protocol. In recent times, the enormous network traffic generated in seconds is difficult to analyze with the traditional rule-based approaches. Therefore, Intrusion detection systems (IDS) are seen as the most important tool to ensure this role. The proposed work focus on 1) Creating a misbehaving of RPL protocol by implementing a rank attack in the network and 2) proposing an IDS based on the multi-Layer Perceptron (MLP) neural network with the aim to verify and classify normal and abnormal network traffic. The experiment achieved a high percentage of training dataset accuracy F1 scores and Recall up to (94.57%), (98%) and (100%), respectively.