@inproceedings { , title = {Unsupervised one-class learning for anomaly detection on home IoT network devices}, abstract = {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.}, conference = {2021 International Conference on Cyber Situational Awareness, Data Analytics and Assessment, CyberSA 2021}, doi = {10.1109/CyberSA52016.2021.9478248}, isbn = {9781665430920}, publicationstatus = {Published}, url = {https://uwe-repository.worktribe.com/output/7341806}, keyword = {Computer Science Research Centre, Creative industries and technologies, Index Terms-IoT, anomaly detection, one-class learning}, year = {2021}, author = {White, Jonathan and Legg, Phil} }