Guest Editorial Introduction to the Special Issue on Anomaly Detection in Emerging Data-Driven Applications: Theory, Algorithms, and Applications

We are delighted to present this special issue on Anomaly Detection in Emerging Data-Driven Applications: Theory, Algorithms, and Applications. Anomaly detection plays an important part of knowledge and data engineering, such as cybersecurity, fintech, healthcare, public security and AI safety. Howe...

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Veröffentlicht in:IEEE transactions on knowledge and data engineering Jg. 35; H. 12; S. 11982 - 11983
Hauptverfasser: Li, Jianxin, He, Lifang, Peng, Hao, Cui, Peng, Aggarwal, Charu C., Yu, Philip S.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.12.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Algorithm design and theory
Algorithms
Anomalies
Anomaly detection
Cybersecurity
Data models
Special issues and sections
ISSN:1041-4347, 1558-2191
Online-Zugang:Volltext
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Zusammenfassung:We are delighted to present this special issue on Anomaly Detection in Emerging Data-Driven Applications: Theory, Algorithms, and Applications. Anomaly detection plays an important part of knowledge and data engineering, such as cybersecurity, fintech, healthcare, public security and AI safety. However, large amounts of data have been generated through different types of objects, and it brings new challenges for anomaly detection research. The purpose of this special issue is to provide a forum for researchers and practitioners to present their latest research findings and engineering experiences in the theoretical foundations, empirical studies, and novel applications.
Bibliographie:SourceType-Scholarly Journals-1
content type line 14
ObjectType-Editorial-2
ObjectType-Commentary-1
ISSN:1041-4347
1558-2191
DOI:10.1109/TKDE.2023.3301582