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：自伊恩·古德费洛（Ian Goodfellow）在2014年论文中提出生成对抗网络以来，该领域的研究呈爆炸式增长。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重要问题空间。从平衡数据集到生成稀有类别中未见过的样本，GAN模型提供了广泛的应用机会。本文对当前恶意软件问题空间中生成对抗网络的研究文献进行了综述。期望读者能通过本文全面了解生成对抗模型为该领域带来的贡献，以及其在恶意软件研究中最适用的领域。综述涵盖了当前相关研究、GAN的不同类别，并总结了近期关于优化GAN以适应不同主题的研究成果，同时指出了未来探索方向。