<div class="csl-bib-body">
<div class="csl-entry">Jiang, J., Chen, C., Xu, Y., Li, P., Jin, F., Ning, D., Murturi, I., & Dustdar, S. (2025). Deep Learning for Anomaly Detection in IoT Time Series. In A. Wahid & P. K. Donta (Eds.), <i>Advanced Techniques for Anomaly Detection : Beyond the Basics</i>. CRC Press. http://hdl.handle.net/20.500.12708/226753</div>
</div>
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dc.identifier.uri
http://hdl.handle.net/20.500.12708/226753
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dc.description.abstract
With the widespread adoption of the Internet of Things (IoT), time-series data is being generated in massive quantities across various industries. In IoT systems, detecting anomalies in time-series data is crucial for ensuring reliability, security, and efficiency. This chapter provides a comprehensive overview of anomaly detection techniques in IoT time-series data, categorising anomalies into three main types: point anomalies, collective anomalies, and contextual anomalies. Firstly, it discusses the significance of anomaly detection in IoT, highlighting its importance in diverse applications such as smart grid, network, and financial sectors. Subsequently, it surveys commonly used anomaly detection methods, including statistical approaches, machine learning algorithms, and deep learning models, outlining their principles, advantages, and limitations. Furthermore, challenges and future directions in IoT anomaly detection are discussed, addressing issues such as data heterogeneity, scalability, interpretability, and real-time processing. This review serves as a valuable resource for researchers, practitioners, and stakeholders interested in understanding and deploying anomaly detection techniques for IoT time-series data.
en
dc.language.iso
en
-
dc.subject
Anomaly detection
en
dc.subject
Time series
en
dc.subject
Deep learning
en
dc.subject
Internet of Things
en
dc.title
Deep Learning for Anomaly Detection in IoT Time Series
en
dc.type
Book Contribution
en
dc.type
Buchbeitrag
de
dc.contributor.affiliation
Zhejiang University, China
-
dc.contributor.affiliation
Zhejiang University, China
-
dc.contributor.affiliation
CAST, China
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dc.contributor.affiliation
State Grid Jingdezhen Power Supply Company, China
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dc.relation.isbn
9781003463559
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dc.relation.doi
10.1201/9781003463559
-
dc.type.category
Edited Volume Contribution
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tuw.booktitle
Advanced Techniques for Anomaly Detection : Beyond the Basics
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tuw.peerreviewed
true
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tuw.relation.publisher
CRC Press
-
tuw.relation.publisherplace
Boca Raton
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tuw.book.chapter
5
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tuw.researchTopic.id
I4
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tuw.researchTopic.name
Information Systems Engineering
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tuw.researchTopic.value
100
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tuw.publication.orgunit
E194-02 - Forschungsbereich Distributed Systems
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dc.description.numberOfPages
39
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tuw.author.orcid
0000-0003-0240-3834
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tuw.author.orcid
0000-0001-6872-8821
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tuw.editor.orcid
0000-0002-8233-6071
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wb.sciencebranch
Informatik
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wb.sciencebranch.oefos
1020
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wb.sciencebranch.value
100
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http://purl.org/coar/resource_type/c_3248
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restricted
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Publications
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book part
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item.languageiso639-1
en
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no Fulltext
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crisitem.author.dept
E194-02 - Forschungsbereich Distributed Systems
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crisitem.author.dept
E194-02 - Forschungsbereich Distributed Systems
-
crisitem.author.dept
Zhejiang University, China
-
crisitem.author.dept
CAST, China
-
crisitem.author.dept
State Grid Jingdezhen Power Supply Company, China
-
crisitem.author.dept
E194-02 - Forschungsbereich Distributed Systems
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crisitem.author.dept
E194-02 - Forschungsbereich Distributed Systems
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crisitem.author.orcid
0000-0003-0240-3834
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crisitem.author.orcid
0000-0001-6872-8821
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crisitem.author.parentorg
E194 - Institut für Information Systems Engineering
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crisitem.author.parentorg
E194 - Institut für Information Systems Engineering
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crisitem.author.parentorg
E194 - Institut für Information Systems Engineering
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crisitem.author.parentorg
E194 - Institut für Information Systems Engineering