Foundation Models for Anomaly Detection: Vision and Challenges

TL;DR

This paper reviews recent advances in foundation models for anomaly detection across various domains, highlighting their capabilities, classifications, challenges, and future research directions.

Contribution

It provides the first comprehensive survey and taxonomy of FM-based anomaly detection methods, analyzing state-of-the-art techniques and identifying key challenges.

Findings

FMs improve anomaly detection accuracy and interpretability

Classification of FMs into encoders, detectors, and interpreters

Discussion of challenges and future research directions

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.