Comparison of ML Algorithms to Detect Vulnerabilities of RPL-Based IoT Devices in Intelligent and Fuzzy Systems

Abstract

AbstractThe RPL protocol (Routing Protocol for Low-Power and Lossy Networks) was designed by IETF [1] for 6LoWPAN to optimize power consumption on the Internet of Things (IoT) devices. These devices have limited processing power, memory, and generally limited energy because they are battery-powered. RPL aims to establish the shortest distance by setting up n number of IoT devices through each other DAG (Directed Acyclic Graph) and therefore the most optimum energy consumption. However, due to the complex infrastructure of RPL and the low capacity of IoT devices, the RPL protocol operating at the network layer is susceptible to attacks. Therefore, it is vital to develop a fast, practical, uncomplicated, and reliable intrusion detection system in the network layer. In the event of an attack on IoT devices operating with the RPL protocol, an anomaly will occur in the network packets in the 3rd layer. Processing these packages with machine learning algorithms will make the detection of the attack extremely easy. In this article, “Decision Tree,” (DT) “Logistic Regression,” (LR) “Random Forest,” (RF) “Fuzzy Pattern Tree Classifier,” (FPTC), and “Neural Network” (NN) algorithms are compared for catching Flooding Attacks (FA), Version Number Increase Attacks (VNIA), and Decreased Rank (DRA) attacks. At the end of our study, it is observed that the Random forest algorithm gave better results than other algorithms in the system built by the study.KeywordsRPL attacksMachine learning algorithmsHello flood attackDecreased rank attackVersion number increase attack