This paper provides a comprehensive survey of the application of Generative Artificial Intelligence (GAI) in enhancing security within the physical layer of communication networks. GAI, known for its ability to generate diverse content and learn complex data distributions, offers significant advantages in addressing security challenges that traditional AI approaches often struggle with. The paper highlights the importance of advanced GAI models such as Generative Adversarial Networks (GANs), Autoencoders (AEs), Variational Autoencoders (VAEs), and Diffusion Models (DMs) in improving communication confidentiality, authentication, availability, resilience, and integrity. The authors delve into how GAI can address the challenges of physical layer security, including data sparsity and incompleteness, by leveraging its capabilities in data reconstruction and augmentation. They also outline future research directions, focusing on model improvements, multi-scenario deployment, resource-efficient optimization, and secure semantic communication. Key contributions of the paper include: 1. A detailed analysis of how GAI models can enhance key security properties. 2. Exploration of GAI's role in addressing data sparsity and incompleteness in physical layer security. 3. Identification of future research directions, including model improvements, multi-scenario deployment, resource-efficient optimization, and secure semantic communication. The paper is structured into several sections, covering Communication Confidentiality and Authentication, Communication Availability and Resilience, and Communication Integrity. Each section provides an overview of the relevant concepts, existing methods, and recent advancements in GAI applications for physical layer security. The authors also discuss the limitations and challenges of current approaches, emphasizing the need for more generalizable and interpretable models. Overall, the paper underscores the potential of GAI to significantly enhance the security of communication networks at the physical layer, making it a valuable resource for researchers and practitioners in the field.This paper provides a comprehensive survey of the application of Generative Artificial Intelligence (GAI) in enhancing security within the physical layer of communication networks. GAI, known for its ability to generate diverse content and learn complex data distributions, offers significant advantages in addressing security challenges that traditional AI approaches often struggle with. The paper highlights the importance of advanced GAI models such as Generative Adversarial Networks (GANs), Autoencoders (AEs), Variational Autoencoders (VAEs), and Diffusion Models (DMs) in improving communication confidentiality, authentication, availability, resilience, and integrity. The authors delve into how GAI can address the challenges of physical layer security, including data sparsity and incompleteness, by leveraging its capabilities in data reconstruction and augmentation. They also outline future research directions, focusing on model improvements, multi-scenario deployment, resource-efficient optimization, and secure semantic communication. Key contributions of the paper include: 1. A detailed analysis of how GAI models can enhance key security properties. 2. Exploration of GAI's role in addressing data sparsity and incompleteness in physical layer security. 3. Identification of future research directions, including model improvements, multi-scenario deployment, resource-efficient optimization, and secure semantic communication. The paper is structured into several sections, covering Communication Confidentiality and Authentication, Communication Availability and Resilience, and Communication Integrity. Each section provides an overview of the relevant concepts, existing methods, and recent advancements in GAI applications for physical layer security. The authors also discuss the limitations and challenges of current approaches, emphasizing the need for more generalizable and interpretable models. Overall, the paper underscores the potential of GAI to significantly enhance the security of communication networks at the physical layer, making it a valuable resource for researchers and practitioners in the field.