Gespeichert in:
| Hauptverfasser: | , , , , , , , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2025
|
| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2508.01983 |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866913974153379840 |
|---|---|
| author | Hu, Chenbo Zhang, Ruichen Li, Bo Jiang, Xu Zhao, Nan Di Renzo, Marco Niyato, Dusit Nallanathan, Arumugam Karagiannidis, George K. |
| author_facet | Hu, Chenbo Zhang, Ruichen Li, Bo Jiang, Xu Zhao, Nan Di Renzo, Marco Niyato, Dusit Nallanathan, Arumugam Karagiannidis, George K. |
| contents | 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 of SAGINs. Three step-by-step tutorials discuss how to apply GAI to solve specific problems using concrete methods, emphasizing its generative paradigm beyond traditional AI. Finally, we outline open issues and future research directions, including lightweight deployment, adversarial robustness, and cross-domain governance, to provide major insights into GAI's role in shaping next-generation SAGIN security. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_01983 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Generative AI-Empowered Secure Communications in Space-Air-Ground Integrated Networks: A Survey and Tutorial Hu, Chenbo Zhang, Ruichen Li, Bo Jiang, Xu Zhao, Nan Di Renzo, Marco Niyato, Dusit Nallanathan, Arumugam Karagiannidis, George K. Cryptography and Security 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 of SAGINs. Three step-by-step tutorials discuss how to apply GAI to solve specific problems using concrete methods, emphasizing its generative paradigm beyond traditional AI. Finally, we outline open issues and future research directions, including lightweight deployment, adversarial robustness, and cross-domain governance, to provide major insights into GAI's role in shaping next-generation SAGIN security. |
| title | Generative AI-Empowered Secure Communications in Space-Air-Ground Integrated Networks: A Survey and Tutorial |
| topic | Cryptography and Security |
| url | https://arxiv.org/abs/2508.01983 |