With the rapid development of emerging technologies and applications, large amounts of data have been generated through different types of objects, such as texts, images, graphs, and videos. This scenario has led to a renewed attention in anomaly detection and security issues, which are indispensable in many fields like cybersecurity, fintech, healthcare, public security and AI safety. Recently, various studies propose to leverage the power of machine learning and data analysis for anomaly detection, which have shown some promising results. However, many challenging problems still remain unsolved due to the complex nature of data.
This special issue will solicit recent advances in anomaly detection that exploit the data structures, semantics, dynamics, and heterogeneity to provide more reliable and efficient anomaly detection systems. This special issue solicits original and high-quality papers that address emerging research challenges in anomaly detection. Potential topics include, but are not limited to, the following:
Submission Deadline: May 31, 2021
Completion of 1st Round of Reviews: August 18, 2021
Minor Revisions Due: October 18, 2021
Completion of 2nd Round of Reviews: November 18, 2021
Editorial Decisions Sent: December 31, 2021
Planned Publication: Late 2022
Submitting authors should the TKDE Author Information page. Note that mandatory overlength page charges and color charges will apply. Manuscripts should be submitted electronically
Jianxin Li, Beihang University, China, lijx@act.buaa.edu.cn
Lifang He, Lehigh University, USA, lih319@lehigh.edu
Hao Peng, Beihang University, China, penghao@buaa.edu.cn
Peng Cui, Tsinghua University, China, cuip@tsinghua.edu.cn
Charu C. Aggarwal, IBM Research, USA, charu@us.ibm.com
Philip S. Yu, University of Illinois at Chicago, USA, psyu@uic.edu