Name
Title | ||
|---|---|---|
Applications of Generative Adversarial Networks in Anomaly Detection: A Systematic Literature Review |
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 tumors. Over time, many anomaly detection techniques have been introduced. However, in general, they all suffer from the same problem: 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 much attention in anomaly detection research, due to their unique ability to generate new data. In this paper, we present a systematic review of the literature in this area, covering 128 papers. The goal of this review paper is to analyze the relation between anomaly detection techniques and types of GANs, to identify the most common application domains for GAN-assisted and GAN-based anomaly detection, and to assemble information on datasets and performance metrics used to assess them. 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. In summary, GANs are used in anomaly detection to address the problem of insufficient amount of data for the anomalous behaviour, either through data augmentation or representation learning. The most commonly used GAN architectures are DCGANs, standard GANs, and cGANs. The primary application domains include medicine, surveillance and intrusion detection. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/ACCESS.2021.3131949 | IEEE ACCESS |
Keywords | DocType | Volume |
Anomaly detection, Data models, Systematics, Generative adversarial networks, Generators, Representation learning, Training, Anomaly detection, data augmentation, generative adversarial networks, outlier detection, representation learning | Journal | 9 |
ISSN | Citations | PageRank |
2169-3536 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mikael Sabuhi | 1 | 0 | 0.68 |
| Ming Zhou | 2 | 0 | 0.34 |
| Cor-Paul Bezemer | 3 | 1 | 0.69 |
| Petr Musilek | 4 | 0 | 0.34 |