Generative AI-Empowered Secure Communications in Space-Air-Ground Integrated Networks: A Survey and Tutorial

2025-08-03

AI Summary

Traditional security mechanisms and discriminative AI approaches fail in Space-Air-Ground Integrated Networks (SAGINs) due to their multidimensional heterogeneity and highly dynamic topologies. This paper proposes the first generative AI–driven, cross-layer security framework for SAGINs, overcoming three critical bottlenecks: semantic understanding, controllable synthetic data generation, and autonomous cross-domain decision-making. The framework integrates physical- to network-layer security mechanisms—incorporating adversarial robust training, lightweight model deployment, and cross-domain governance policies—to significantly enhance communication authenticity, confidentiality, integrity, and availability. Key contributions include: (1) a systematic analysis of generative AI’s technical pathways and open challenges for SAGIN security; (2) three reproducible, hands-on technical tutorials; and (3) foundational theory and practical paradigms for next-generation intelligent secure communications.

Technology Category

• Computer Vision: Generative Adversarial Networks (GANs) for Vision
• Philosophy and Ethics of AI: Artificial General Intelligence
• Multiagent Systems: Agent Communication

Application Category

• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Security and Privacy: Security and privacy of machine learning and AI applications
• Search and Retrieval-Augmented AI: Agentic search

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 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.

Problem

• Address security challenges in SAGINs using Generative AI
• Overcome failures in authenticity, confidentiality, and integrity
• Explore lightweight deployment and adversarial robustness solutions

Innovation

• Generative AI enhances SAGIN security defenses
• GAI mitigates failures across multiple network layers
• Step-by-step tutorials apply GAI to specific problems