The 2nd International Workshop on Anomaly Detection with Machine Learning

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
  • Abnormal risk prediction
  • Abnormal pattern mining
  • Explainability of anomaly detection
  • Deep learning and Graph learning
  • Application of anomaly detection

Organizers

  • Jason J. Jung, Chung-Ang University (South Korea) (j2jung@gmail.com)
  • Gen Li, Chengdu University (China)
  • Szymon Bobek, Jagiellonian University (Poland)
  • Grzegorz Nalepa, Jagiellonian University (Poland)

Submission

See submission instructions for the conference at the call for papers. At the beginning of the submission, please choose the track “The 2nd International Workshop on Anomaly Detection with Machine Learning”.

Special Session Papers Submission Deadline: June 15th, 2025