Cook, A, Misirli, G ORCID: https://orcid.org/0000-0002-2454-7188 and Fan, Z (2019) Anomaly Detection for IoT Time-Series Data: A Survey. IEEE Internet of Things.

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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.

Item Type: Article
Additional Information: The final accepted manuscript and all relevant information you require regarding copyright can be found at; https://ieeexplore.ieee.org/document/8926446/metrics#metrics
Uncontrolled Keywords: anomoly detection
Subjects: Q Science > Q Science (General)
Q Science > QA Mathematics
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Natural Sciences > School of Computing and Mathematics
Depositing User: Symplectic
Date Deposited: 06 May 2020 13:52
Last Modified: 06 May 2020 13:52
URI: https://eprints.keele.ac.uk/id/eprint/7576

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