RPL Routing Attacks Detection for IoT Networks Using Machine Learning

Cyberattacks on Internet of Things (IoT) networks are escalating rapidly due to the widespread use of these networks and the lack of adequate security measures derived from inherent constraints, such as those related to power consumption. Consequently, considering their unique characteristics, a robust methodology is critical for detecting IoT attacks. Many IoT attacks target the RPL routing protocol designed for low-power and lossy networks. In this study, we propose and evaluate the effectiveness of the Random Forest classifier and KNN classifier in detecting four types of attacks: Hello flooding attack, version number attack, Blackhole attack, and decreased rank attack. The analysis utilizes a publicly available dataset containing over 160 million records. Experimental results demonstrate a 99% accuracy rate using the Random Forest algorithm and a 98% accuracy rate using the KNN algorithm.