On the Fence: Anomaly Detection in IoT Networks

The Internet of Things (IoT) is increasingly impacting every aspect of life, with deployment in various societal applications. This paper explores anomaly detection via novelty and outlier detection approaches for IoT networks. To this end, three unsupervised learning algorithms, namely Isolation Forest (IF), Local Outlier Factor (LOF), and One-Class Support Vector Machine (OSVM), are evaluated on three publicly available IoT datasets. The results demonstrate that when the proposed solution leverages LOF to embrace the novelty approach by considering only pure benign data for training, it achieves high performance with Fl-scores within the range of 84% to 94%.