Detection of Hello Flooding Attacks on RPL in Internet of Things Networks Using Different Machine Learning Algorithms

Abstract

In Internet of Things (IoT), routing protocols by design are quite vulnerable to attacks. These attacks can be designed to reduce bandwidth, corrupt information, and/or threaten the integrity of the network. Hence, it is crucial to identify the attacks, mitigate them, and prevent further damage to the network. In this paper, we have proposed a methodology to identify Hello Flooding (HF) attacks using various machine learning classification techniques. The paper has a primary focus on dataset creation, followed by the implementation of various machine learning algorithms, using which we not only identify if a network is under attack, but also identify malicious nodes and the nodes affected by the malicious nodes.