Jonathan White Jonathan6.White@uwe.ac.uk
Senior Lecturer in Cyber Security
Jonathan White Jonathan6.White@uwe.ac.uk
Senior Lecturer in Cyber Security
Professor Phil Legg Phil.Legg@uwe.ac.uk
Professor in Cyber Security
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.
Presentation Conference Type | Conference Paper (published) |
---|---|
Conference Name | 2021 International Conference on Cyber Situational Awareness, Data Analytics and Assessment, CyberSA 2021 |
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 |
Unsupervised One-Class Learning For Anomaly Detection On Home IoT Network Devices
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