This paper investigates the feasibility and effectiveness of employing Generative Adversarial Networks (GANs) for the generation of decoy configurations in the field of cyber defense. The utilization of honeypots has been extensively studied in the past; however, selecting appropriate decoy configurations for a given cyber scenario (and subsequently retrieving/generating them) remain open challenges. Existing approaches often rely on maintaining lists of configurations or storing collections of pre-configured images, lacking adaptability and efficiency. In this pioneering study, we present a novel approach that leverages GANs' learning capabilities to tackle these challenges. To the best of our knowledge, no prior attempts have been made to utilize GANs specifically for generating decoy configurations. Our research aims to address this gap and provide cyber defenders with a powerful tool to bolster their network defenses.

翻译：本文研究了在网络安全防御领域应用生成对抗网络（GANs）生成诱饵配置的可行性与有效性。蜜罐技术在过去已得到广泛研究，然而针对特定网络场景选择适当的诱饵配置（并随之检索/生成）仍是亟待解决的挑战。现有方法通常依赖于维护配置列表或存储预配置镜像集合，缺乏适应性与效率。在这项开创性研究中，我们提出了一种利用GANs学习能力应对这些挑战的新方法。据我们所知，此前尚未有专门利用GANs生成诱饵配置的尝试。本研究旨在填补这一空白，为网络防御者提供强化网络防御的有力工具。