Generative Adversarial Networks for Malware Detection: a Survey

TL;DR

This survey reviews how Generative Adversarial Networks are applied in malware research, highlighting their roles in data balancing, generating unseen malware examples, and optimizing detection methods.

Contribution

It provides a comprehensive overview of GAN applications in malware detection, categorizes different GAN types, and discusses recent research outcomes and future research directions.

Findings

GANs aid in balancing malware datasets.

GANs generate unseen malware samples.

Recent research optimizes GANs for malware detection.

Abstract

Since their proposal in the 2014 paper by Ian Goodfellow, there has been an explosion of research into the area of Generative Adversarial Networks. While they have been utilised in many fields, the realm of malware research is a problem space in which GANs have taken root. From balancing datasets to creating unseen examples in rare classes, GAN models offer extensive opportunities for application. This paper surveys the current research and literature for the use of Generative Adversarial Networks in the malware problem space. This is done with the hope that the reader may be able to gain an overall understanding as to what the Generative Adversarial model provides for this field, and for what areas within malware research it is best utilised. It covers the current related surveys, the different categories of GAN, and gives the outcomes of recent research into optimising GANs for different topics, as well as future directions for exploration.