Detecting Routing Protocol Low Power and Lossy Network Attacks Using Machine Learning Techniques

The Internet of Things (loT) has been regarded as the most critical technology due to its resource-constrained sensors transmitted via low-power wireless technologies beneath low-power lossy networks (LLNs), where the LLN has high latency and lower throughput due to its traffic patterns. The loT possesses low-cost and low-power sensor technology, which is characterised by its low energy consumption and low latency. In this study, machine learning (ML) techniques (XGBoost, NB, and LDA) are proposed for detecting low power lossy network (RPL) attacks of routing protocol utilising multiclass of normal and four different routing attacks (Flooding Attack, Blackhole Attack, Decreased Rank Attack, and DODAG version number attack). Through experimentation and evaluation, the XGBoost classifier demonstrated superior performance, achieving an accuracy of 92.45%.