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Unsupervised one-class learning for anomaly detection on home IoT network devices

White, Jonathan; Legg, Phil

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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.

Citation

White, J., & Legg, P. (2021). Unsupervised one-class learning for anomaly detection on home IoT network devices. In 2021 International Conference on Cyber Situational Awareness, Data Analytics and Assessment (CyberSA). https://doi.org/10.1109/CyberSA52016.2021.9478248

Conference Name 2021 International Conference on Cyber Situational Awareness, Data Analytics and Assessment, CyberSA 2021
Conference Location Dublin, Ireland
Start Date Jun 14, 2021
End Date Jun 18, 2021
Acceptance Date Apr 16, 2021
Online Publication Date Jul 12, 2021
Publication Date Jul 12, 2021
Deposit Date May 7, 2021
Publicly Available Date Sep 13, 2021
Book Title 2021 International Conference on Cyber Situational Awareness, Data Analytics and Assessment (CyberSA)
ISBN 9781665430920
DOI https://doi.org/10.1109/CyberSA52016.2021.9478248
Keywords Index Terms-IoT; anomaly detection; one-class learning
Public URL https://uwe-repository.worktribe.com/output/7341806

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