Submissions due: 1 August 2022
Publication: March/April 2023
Anomalies (a.k.a. outliers) commonly exist in various real-world scenarios, such as fraud in finance and insurance, intrusion in cybersecurity, fault in safety-critical systems, bushfire early warning, disease outbreak control, fake news, images and videos in social media, and medical diagnosis. Some anomalies could cause disasters that lead to immense economic loss or even deaths unless discovered and dealt with on time. These applications make anomaly analytics increasingly relevant in the modern world. Due to its foremost importance, the study of anomaly detection has a long history and has created a wealth of anomaly detection methods. With the advent of big data, new challenges and questions are introduced, which inspires novel ways of developing algorithms, methods, and techniques to foster the analysis, modeling, interpretation, and prediction as well as detection of anomalies.
Recent years have witnessed rapid growth in the number of academics and practitioners interested in artificial intelligence (AI) for anomaly detection. In particular, various deep learning models have been developed for anomaly detection. In many cases, however, deep models are hard to tune and hard to interpret. In addition, little attention has been paid to other aspects/phases (rather than anomaly detection) of the whole lifecycle of anomaly analytics. On the other hand, the increasing complexity of real-world cyber-physical systems is giving rise to unprecedented challenges facing anomaly analytics.
This special issue aims to promote innovative AI research and development that address key challenges for detecting, describing, modelling, predicting, understanding, suppressing, and eliminating anomalies in various application domains. Topics of interest include, but are not limited to:
- Foundations and principles of anomaly analytics
- Novel AI models and algorithms for anomaly analytics
- Graph learning for anomaly analytics
- Deep learning techniques for anomaly detection
- Anomaly modelling, analysis, and (deep) understanding
- Descriptive, predictive, and prescriptive analytics of anomalies
- Augmented intelligence for anomaly detection
- Human-in-the-loop machine learning for anomaly detection
- Trustworthy anomaly analytics
- Fairness, transparency, and explainability
- Privacy, safety, and security
- Tools, platforms, and systems for deep anomaly analytics
- Anomaly analytics in various domains
For author information and guidelines on submission criteria, please visit the IS Author Information page. Please submit papers through the ScholarOne system, and be sure to select the special-issue name. Manuscripts should not be published or currently submitted for publication elsewhere. Please submit only full papers intended for review, not abstracts, to the ScholarOne portal.
Contact the guest editors at firstname.lastname@example.org.
- Feng Xia, Federation University Australia (Australia)
- Leman Akoglu, Carnegie Mellon University (USA)
- Charu Aggarwal, IBM T. J. Watson Research Center (USA)
- Huan Liu, Arizona State University (USA)