Generative AI for Secure Physical Layer Communications: A Survey
CoRR(2024)
摘要
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.
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