Unsupervised One-Class Learning for Anomaly Detection on Home IoT Network Devices
Jonathan White, Phil Legg · 2021
In this paper we study anomaly detection methods for home IoT devices. Specifically, we address unsupervised one-class learning methods due to their ability to learn deviations from a single normal class. In a home IoT environment, this consideration is crucial as supervised methods would result in a burden on many non-technical consumers which could hinder their effectiveness. For our study, we develop a home IoT network monitoring tool, and we illustrate network attacks against a variety of typical home IoT devices. As a result, we propose measures that could aid home consumers in defending ever-increasing home IoT networks.