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Ian Goodfellow在2014年的论文中提出建议以来,对创能反逆网络领域的研究迅速展开,虽然这些研究已在许多领域得到利用,但恶意软件研究领域是一个问题空间,GANs已经扎根。从平衡数据集到在稀有类别中创建未知实例,GAN模型提供了广泛的应用机会。本文调查了当前用于在恶意软件问题空间使用创能反逆网络的研究和文献,希望读者能够全面了解《创能反逆网络模型》为该领域提供的内容,以及它最佳利用的恶意软件研究领域。它涵盖了当前的相关调查,GAN的不同类别,并提供了近期研究结果,为不同专题优化GANs以及未来探索方向提供了优化GANs的研究成果。</s>