Anomaly Detection for IoT Networks: Empirical Study

The Internet of Things (IoT) actively transforms physical objects, including portable, wearable, and implantable sensors, into an information ecosystem that enriches the technology and data in every aspect of life. This paper examines two anomaly detection approaches: novelty and outlier, for IoT networks. In this respect, we leverage four unsupervised learning algorithms, namely Isolation Forest (IF), Local Outlier Factor (LOF), One-Class Support Vector Machine (OSVM), and variational encoder (AE), on four publicly available IoT datasets. The experiments reveal that by embracing the novelty approach by considering only pure benign data for training, the AE model achieves a high F1-score and AUC up to 97% and 0.97.