Deep Anomaly Analytics: Advancing the Frontier of Anomaly Detection

Deep anomaly analytics is a rapidly evolving field that leverages the power of deep learning to identify anomalies in various datasets. The use of deep anomaly analytics has increased significantly in recent years due to the growing need to detect anomalies in complex data that traditional methods struggle to handle. Deep anomaly analytics has the potential to transform various industries, including, e. g. , healthcare, finance, and cybersecurity, by providing valuable insights and helping to diagnose diseases, prevent fraud, and detect cyber threats. However, there are also many challenges associated with deep anomaly analytics. This editorial provides an overview of the field of deep anomaly analytics, and highlights a few key challenges facing this field, i. e. , time series anomaly detection, graph anomaly detection, efficiency (of models), and solving real-world problems. Additionally, it serves as an introduction to this special issue that delves further into these topics.

Authors

• Feng Xia RMIT University
• Leman Akoglu
• Charu Aggarwal IBM T. J. Watson Research Center
• Huan Liu Institute of Information Science, Beijing Jiaotong University Visual Intellgence +X International Cooperation Joint Laboratory of MOE

Keywords

No keywords are indexed for this paper.