Deep Learning for Anomaly Detection: A Review

TL;DR

This paper provides a comprehensive review of deep learning methods for anomaly detection, categorizing recent advancements, analyzing their core principles, and discussing future research opportunities in the field.

Contribution

It offers a detailed taxonomy of deep anomaly detection techniques, summarizing their key features, assumptions, and challenges, and highlights future directions.

Findings

Classified deep anomaly detection methods into three high-level categories

Analyzed the advantages and disadvantages of various approaches

Discussed future opportunities and challenges in the field

Abstract

Anomaly detection, a.k.a. outlier detection or novelty detection, has been a lasting yet active research area in various research communities for several decades. There are still some unique problem complexities and challenges that require advanced approaches. In recent years, deep learning enabled anomaly detection, i.e., deep anomaly detection, has emerged as a critical direction. This paper surveys the research of deep anomaly detection with a comprehensive taxonomy, covering advancements in three high-level categories and 11 fine-grained categories of the methods. We review their key intuitions, objective functions, underlying assumptions, advantages and disadvantages, and discuss how they address the aforementioned challenges. We further discuss a set of possible future opportunities and new perspectives on addressing the challenges.