Abstract
Anomaly detection is a critical task in various industries, including cybersecurity, finance, healthcare, and industrial monitoring. Traditional methods of anomaly detection are no longer sufficient to handle the increasing complexity and volume of data being generated. There is a growing interest in applying machine learning techniques to develop more accurate, scalable, and adaptable solutions to detect anomalies in large and complex datasets.
The topic of Anomaly Detection with Machine Learning is of special interest as it addresses the need for more advanced and efficient anomaly detection methods in the face of increasing data complexity. By leveraging the power of machine learning algorithms, researchers and practitioners can develop more accurate, scalable, and adaptable solutions to detect anomalies in large and complex datasets. This workshop aims to explore the latest advancements in anomaly detection using machine learning and provide a platform for sharing insights, experiences, and cutting-edge research in this field.
We invite researchers and practitioners to submit their cutting-edge research on anomaly detection with machine learning to this workshop and be part of the discussion on advancing the state-of-the-art in this important field.
Topics
Topics of interest for this session include but are not limited to:
- Anomaly detection
- Anomaly early detection
- Abnormal risk prediction
- Abnormal pattern mining
- Machine learning
- Deep learning
- Graph learning
- Application of anomaly detection
Organizers
Jason J. Jung, Chung-Ang University (South Korea)
Gen Li, Chengdu University (China)
Longlong Yu, Chengdu University (China)
Submission
See submission instructions for the conference at https://ideal2024.webs.upv.es/submission/
Special Session Papers Submission Deadline: July 26, 2024