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.

翻译：自2014年Ian Goodfellow的论文提出生成对抗网络以来，该领域的研究呈现爆发式增长。虽然生成对抗网络已在多个领域得到应用，但恶意软件研究领域是其扎根的关键问题空间。从平衡数据集到生成稀有类别的未见样本，生成对抗网络模型提供了广泛的应用机会。本文系统梳理了当前在恶意软件问题空间中使用生成对抗网络的研究成果与文献，旨在帮助读者整体把握生成对抗模型为该领域提供的价值，以及其在恶意软件研究中最佳的应用方向。本文涵盖了当前相关综述、生成对抗网络的不同类别，并总结了近期关于优化生成对抗网络以适应不同课题的研究成果，同时展望了未来的探索方向。