Generative Artificial Intelligence (GAI) stands at the forefront of AI innovation, demonstrating rapid advancement and unparalleled proficiency in generating diverse content. Beyond content creation, GAI has significant analytical abilities to learn complex data distribution, offering numerous opportunities to resolve security issues. In the realm of security from physical layer perspectives, traditional AI approaches frequently struggle, primarily due to their limited capacity to dynamically adjust to the evolving physical attributes of transmission channels and the complexity of contemporary cyber threats. This adaptability and analytical depth are precisely where GAI excels. Therefore, in this paper, we offer an extensive survey on the various applications of GAI in enhancing security within the physical layer of communication networks. We first emphasize the importance of advanced GAI models in this area, including Generative Adversarial Networks (GANs), Autoencoders (AEs), Variational Autoencoders (VAEs), and Diffusion Models (DMs). We delve into the roles of GAI in addressing challenges of physical layer security, focusing on communication confidentiality, authentication, availability, resilience, and integrity. Furthermore, we also present future research directions focusing model improvements, multi-scenario deployment, resource-efficient optimization, and secure semantic communication, highlighting the multifaceted potential of GAI to address emerging challenges in secure physical layer communications and sensing.

翻译：生成式人工智能（GAI）作为AI创新的前沿领域，在生成多样化内容方面展现出快速进步和无与伦比的熟练度。除内容创作外，GAI在习得复杂数据分布方面具备强大的分析能力，为解决安全问题提供了众多机遇。从物理层安全视角来看，传统人工智能方法往往难以应对挑战，主要因其能力有限，无法动态适应传输信道持续变化的物理特性以及当代网络威胁的复杂性。而GAI恰恰在适应性与分析深度方面表现卓越。因此，本文对GAI在增强通信网络物理层安全性中的多种应用进行了全面综述。我们首先强调先进GAI模型在此领域的重要性，包括生成对抗网络（GANs）、自编码器（AEs）、变分自编码器（VAEs）以及扩散模型（DMs）。深入探讨了GAI在应对物理层安全挑战中的作用，重点关注通信保密性、认证、可用性、弹性与完整性。此外，本文还提出了未来研究方向，涵盖模型改进、多场景部署、资源高效优化及安全语义通信，凸显了GAI在解决安全物理层通信与感知领域新兴挑战中的多维度潜力。