An Anomaly-Based IDS for Detecting Attacks in RPL-Based Internet of Things

Abstract

The Internet of Things (IoT) is a concept that allows the networking of various objects of everyday life and communications on the Internet without human interaction. The IoT consists of Low-Power and Lossy Networks (LLN) which for routing use a special protocol called Routing over Low-Power and Lossy Networks (RPL). Due to the resource-constrained nature of RPL networks, they may be exposed to a variety of internal attacks. Neighbor attack and DIS attack are the specific internal attacks at this protocol. This paper presents an anomaly-based lightweight Intrusion Detection System (IDS) based on threshold values for detecting attacks on the RPL protocol. The results of the simulation using Cooja show that the proposed model has a very high True Positive Rate (TPR) and in some cases, it can be 100%, while the False Positive Rate (FPR) is very low. The results show that the proposed model is fully effective in detecting attacks and applicable to large-scale networks.