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:
- Theory/foundation of anomaly detection, such as optimization and learnability, anomaly explanation, and reasoning model
- Supervision in anomaly detection, such as semi-supervised anomaly detection, unsupervised anomaly detection, supervised anomaly detection, and ensemble model
- Anomaly detection based on deep learning, such as representation learning-based methods, sequence learning-based methods, reinforcement learning-based methods, transfer learning-based methods, and generative models
- Business problems, such as financial fraud, risk modeling, intrusion/malware detection in systems, early detection of emerging phenomena, usage behavior anomaly detection, fake news and rumor detection in social media, and power grid anomaly detection.
- Human-in-the-loop anomaly detection
- Spatial-temporal data anomaly detection
- Biological and chemical anomaly detection
- Anomaly detection in computer vision
- Adversarial attacks on anomaly detection
- Adversarial robustness on anomaly detection
- Adversarial samples detection
- Anomaly detection in federated learning
Important Dates
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
Submission Guidelines
Submitting authors should the TKDE Author Information page. Note that mandatory overlength page charges and color charges will apply. Manuscripts should be submitted electronically
to ScholarOne Manuscripts.
Guest Editors
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
