Generative AI-Empowered Secure Communications in Space-Air-Ground Integrated Networks: A Survey and Tutorial
Chenbo Hu, Ruichen Zhang, Bo Li, Xu Jiang, Nan Zhao, Marco Di Renzo, Dusit Niyato, Arumugam Nallanathan, and George K. Karagiannidis

TL;DR
This paper surveys how generative AI can enhance security in space-air-ground integrated networks by addressing unique challenges and providing tutorials on practical applications.
Contribution
It offers a comprehensive review of GAI's role in SAGIN security, including advantages, applications, and future research directions, with practical tutorials.
Findings
GAI improves authenticity and confidentiality in SAGINs.
Tutorials demonstrate GAI applications in security problems.
Outlines open challenges like lightweight deployment and robustness.
Abstract
Space-air-ground integrated networks (SAGINs) face unprecedented security challenges due to their inherent characteristics, such as multidimensional heterogeneity and dynamic topologies. These characteristics fundamentally undermine conventional security methods and traditional artificial intelligence (AI)-driven solutions. Generative AI (GAI) is a transformative approach that can safeguard SAGIN security by synthesizing data, understanding semantics, and making autonomous decisions. This survey fills existing review gaps by examining GAI-empowered secure communications across SAGINs. First, we introduce secured SAGINs and highlight GAI's advantages over traditional AI for security defenses. Then, we explain how GAI mitigates failures of authenticity, breaches of confidentiality, tampering of integrity, and disruptions of availability across the physical, data link, and network layers…
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Taxonomy
TopicsUAV Applications and Optimization · Satellite Communication Systems · Advanced Wireless Communication Technologies
