This paper reviews Generative Adversarial Networks (GANs) in detail by discussing the strength of the GAN when compared to other generative models, how GANs works and some of the notable problems with training, tuning and evaluating GANs. The paper also briefly reviews notable GAN architectures like the Deep Convolutional Generative Adversarial Network (DCGAN), and Wasserstein GAN, with the aim of showing how design specifications in these architectures help solve some of the problems with the basic GAN model. All this is done with a view of discussing the application of GANs in cybersecurity studies. Here, the paper reviews notable cybersecurity studies where the GAN plays a key role in the design of a security system or adversarial system. In general, from the review, one can observe two major approaches these cybersecurity studies follow. In the first approach, the GAN is used to improve generalization to unforeseen adversarial attacks, by generating novel samples that resembles adversarial data which can then serve as training data for other machine learning models. In the second approach, the GAN is trained on data that contains authorized features with the goal of generating realistic adversarial data that can thus fool a security system. These two approaches currently guide the scope of modern cybersecurity studies with generative adversarial networks.

中文翻译：

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生成对抗网络综述及其在网络安全中的应用

本文通过讨论 GAN 与其他生成模型相比的优势、GAN 的工作原理以及训练、调整和评估 GAN 的一些显着问题，详细回顾了生成对抗网络 (GAN)。本文还简要回顾了著名的 GAN 架构，如深度卷积生成对抗网络 (DCGAN) 和 Wasserstein GAN，旨在展示这些架构中的设计规范如何帮助解决基本 GAN 模型的一些问题。所有这些都是为了讨论 GAN 在网络安全研究中的应用。在这里，本文回顾了著名的网络安全研究，其中 GAN 在安全系统或对抗系统的设计中起着关键作用。一般来说，从审查中，可以观察到这些网络安全研究遵循的两种主要方法。在第一种方法中，GAN 用于提高对不可预见的对抗性攻击的泛化，通过生成类似于对抗性数据的新样本，然后可以作为其他机器学习模型的训练数据。在第二种方法中，GAN 在包含授权特征的数据上进行训练，目的是生成真实的对抗性数据，从而可以欺骗安全系统。这两种方法目前通过生成对抗网络指导现代网络安全研究的范围。GAN 在包含授权特征的数据上进行训练，目的是生成真实的对抗性数据，从而可以欺骗安全系统。这两种方法目前通过生成对抗网络指导现代网络安全研究的范围。GAN 在包含授权特征的数据上进行训练，目的是生成真实的对抗性数据，从而可以欺骗安全系统。这两种方法目前通过生成对抗网络指导现代网络安全研究的范围。