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

White, Jonathan; Legg, Phil

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