Jonathan White Jonathan6.White@uwe.ac.uk
Senior Lecturer in Cyber Security
Unsupervised one-class learning for anomaly detection on home IoT network devices
White, Jonathan; Legg, Phil
Authors
Professor Phil Legg Phil.Legg@uwe.ac.uk
Professor in Cyber Security
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 | Mar 29, 2024 |
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 |
Files
Unsupervised One-Class Learning For Anomaly Detection On Home IoT Network Devices
(1.3 Mb)
PDF
Licence
http://www.rioxx.net/licenses/all-rights-reserved
Publisher Licence URL
http://www.rioxx.net/licenses/all-rights-reserved
Copyright Statement
© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works
You might also like
Longitudinal risk-based security assessment of docker software container images
(2023)
Journal Article
Interactive cyber-physical system hacking: Engaging students early using Scalextric
(2022)
Presentation / Conference
OGMA: Visualisation for software container security analysis and automated remediation
(2022)
Conference Proceeding
Downloadable Citations
About UWE Bristol Research Repository
Administrator e-mail: repository@uwe.ac.uk
This application uses the following open-source libraries:
SheetJS Community Edition
Apache License Version 2.0 (http://www.apache.org/licenses/)
PDF.js
Apache License Version 2.0 (http://www.apache.org/licenses/)
Font Awesome
SIL OFL 1.1 (http://scripts.sil.org/OFL)
MIT License (http://opensource.org/licenses/mit-license.html)
CC BY 3.0 ( http://creativecommons.org/licenses/by/3.0/)
Powered by Worktribe © 2024
Advanced Search