Goksel Misirli g.misirli@keele.ac.uk
Anomaly Detection for IoT Time-Series Data: A Survey
Misirli; Fan
Authors
Fan
Abstract
Abstract—Anomaly detection is a problem with applications
for a wide variety of domains, it involves the identification of novel or unexpected observations or sequences within the data being captured. The majority of current anomaly detection methods are highly specific to the individual use-case, requiring expert knowledge of the method as well as the situation to which it is being applied. The IoT as a rapidly expanding field offers many
opportunities for this type of data analysis to be implemented however, due to the nature of the IoT this may be difficult. This review provides a background on the challenges which may be encountered when applying anomaly detection techniques to IoT data, with examples of applications for IoT anomaly detection taken from the literature. We discuss a range of approaches which
have been developed across a variety of domains, not limited to Internet of Things due to the relative novelty of this application. Finally we summarise the current challenges being faced in the anomaly detection domain with a view to identifying potential research opportunities for the future.
Acceptance Date | Dec 5, 2019 |
---|---|
Publication Date | Dec 6, 2019 |
Publicly Available Date | Mar 29, 2024 |
Journal | IEEE Internet of Things |
Publisher | Institute of Electrical and Electronics Engineers (IEEE) |
DOI | https://doi.org/10.1109/JIOT.2019.2958185 |
Keywords | anomoly detection |
Publisher URL | https://ieeexplore.ieee.org/document/8926446 |
Files
08926446.pdf
(385 Kb)
PDF
Publisher Licence URL
https://creativecommons.org/licenses/by-nc/4.0/
You might also like
libSBOLj3: a graph-based library for design and data exchange in synthetic biology.
(2023)
Journal Article
Modelling the fitness landscapes of a SCRaMbLEd yeast genome.
(2022)
Journal Article
Reflections on the 35th BCS Human-Computer Interaction Conference at Keele University
(2022)
Conference Proceeding
Virtual Parts Repository 2: Model-Driven Design of Genetic Regulatory Circuits
(2021)
Journal Article
Synthetic biology open language visual (SBOL visual) version 3.0.
(2021)
Journal Article
Downloadable Citations
About Keele Repository
Administrator e-mail: research.openaccess@keele.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