  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.
