Applications of Generative Adversarial Networks in Anomaly Detection: A Systematic Literature Review

TL;DR

This systematic review analyzes how generative adversarial networks (GANs) are applied to anomaly detection across various domains, highlighting techniques, datasets, and future research directions.

Contribution

It provides a comprehensive summary of 128 papers on GAN-based anomaly detection, identifying application domains, techniques, and evaluation methods, and proposes a future research roadmap.

Findings

GANs are effectively used to generate anomalous data for detection.

Application domains include finance, healthcare, and cybersecurity.

Performance metrics vary across studies, with accuracy and precision being common.

Abstract

Anomaly detection has become an indispensable tool for modern society, applied in a wide range of applications, from detecting fraudulent transactions to malignant brain tumours. Over time, many anomaly detection techniques have been introduced. However, in general, they all suffer from the same problem: a lack of data that represents anomalous behaviour. As anomalous behaviour is usually costly (or dangerous) for a system, it is difficult to gather enough data that represents such behaviour. This, in turn, makes it difficult to develop and evaluate anomaly detection techniques. Recently, generative adversarial networks (GANs) have attracted a great deal of attention in anomaly detection research, due to their unique ability to generate new data. In this paper, we present a systematic literature review of the applications of GANs in anomaly detection, covering 128 papers on the subject. The goal of this review paper is to analyze and summarize: (1) which anomaly detection techniques can benefit from certain types of GANs, and how, (2) in which application domains GAN-assisted anomaly detection techniques have been applied, and (3) which datasets and performance metrics have been used to evaluate these techniques. Our study helps researchers and practitioners to find the most suitable GAN-assisted anomaly detection technique for their application. In addition, we present a research roadmap for future studies in this area.