Deep Learning for Anomaly Detection: A Survey

TL;DR

This survey comprehensively reviews deep learning methods for anomaly detection, categorizing techniques, discussing their assumptions, advantages, limitations, and challenges across various application domains.

Contribution

It provides a structured overview of deep learning-based anomaly detection methods, categorizes them, and discusses their effectiveness, assumptions, and open research issues.

Findings

Deep learning techniques vary based on underlying assumptions.

Different methods have distinct advantages and limitations.

Open challenges include computational complexity and domain adaptation.

Abstract

Anomaly detection is an important problem that has been well-studied within diverse research areas and application domains. The aim of this survey is two-fold, firstly we present a structured and comprehensive overview of research methods in deep learning-based anomaly detection. Furthermore, we review the adoption of these methods for anomaly across various application domains and assess their effectiveness. We have grouped state-of-the-art research techniques into different categories based on the underlying assumptions and approach adopted. Within each category we outline the basic anomaly detection technique, along with its variants and present key assumptions, to differentiate between normal and anomalous behavior. For each category, we present we also present the advantages and limitations and discuss the computational complexity of the techniques in real application domains. Finally, we outline open issues in research and challenges faced while adopting these techniques.