Foundation Models for Anomaly Detection: Vision and Challenges

Foundation Models for Anomaly Detection: Vision and Challenges Jing Ren 1, T ao T ang 2, Hong Jia 3, Haytham Fayek 1, Xiaodong Li 1, Suyu Ma 4, Xiwei Xu 4, and Feng Xia 1 1 RMIT University, Australia 2 University of South Australia, Australia 3 University of Melbourne, Australia 4 CSIRO's Data61, Australia {jing.ren, tao.tang }@ieee.org, Abstract As data continues to grow in volume and complexity across domains such as finance, manufacturing, and healthcare, effective anomaly detection is essential for identifying irregular patterns that may signal critical issues. Recently, foundation models (FMs) have emerged as a powerful tool for advancing anomaly detection. They have demonstrated unprecedented capabilities in enhancing anomaly identification, generating detailed data descriptions, and providing visual explanations. This survey presents the first comprehensive review of recent advancements in FM-based anomaly detection. We propose a novel taxonomy that classifies FMs into three categories based on their roles in anomaly detection tasks, i.e., as encoders, detectors, or interpreters. We provide a systematic analysis of state-of-the-art methods and discuss key challenges in leveraging FMs for improved anomaly detection. We also outline future research directions in this rapidly evolving field. 1 Introduction Anomaly detection, also known as outlier detection, is the process of identifying patterns or events in data that significantly deviate from expected behavior [ Chandola et al., 2009] .