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

被引：0

作者：

Li, Jianxin ^{[1
]}

He, Lifang ^{[2
]}

Peng, Hao ^{[1
]}

Cui, Peng ^{[3
]}

Aggarwal, Charu C. ^{[4
]}

Yu, Philip S. ^{[5
]}

机构：

[1] Beihang Univ, Beijing 100191, Peoples R China

[2] Lehigh Univ, Bethlehem, PA USA

[3] Tsinghua Univ, Beijing 100190, Peoples R China

[4] IBM Res, Yorktown Hts, NY 10598 USA

[5] Univ Illinois, Chicago, IL 60607 USA

来源：

IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING | 2023年 / 35卷 / 12期

关键词：

Special issues and sections; Anomaly detection; Data models; Algorithm design and theory;

D O I：

10.1109/TKDE.2023.3301582

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

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页码：11982 / 11983

页数：2

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