An Integrated Survey of Intelligent Security Frameworks for 6G Communication Networks: Reinforcement Learning, Privacy Preservation, Edge Intelligence, and Cross-Layer Optimization

Abstract

With 6th-generation (6G) wireless communication networks on the horizon, a new era of ultra-low latency, massive device connectivity, intelligent automation, and human-to-human (H2H), human-to-machine (H2M) and machine-to-machine (M2M) communication convergence is approaching. 6G promises new horizons of next-generation applications, ranging from autonomous vehicles, smart healthcare, digital twins, and industry automation, to virtual realities and smart environments. However, these transformative features pose considerable security and privacy challenges. Because the traditional security solutions for previous generations of wireless systems are often insufficient for 6G networks, which are dynamic, heterogeneous, and massive in nature, adaptive and intelligent security solutions are essential to contend with increasingly complex threats and adapt to dynamic conditions. In this survey paper, we provide a detailed review of recently developed intelligent security frameworks for 6G wireless communication networks. Specifically, we examine reinforcement learning (RL)-based security, privacy-preserving mechanisms, edge intelligence, and cross-layer optimization techniques.
Reinforcement learning (RL) and deep reinforcement learning (DRL) techniques are explored as suitable approaches for providing adaptive intrusion detection and prevention systems, intrusion detection/avoidance systems (ID/ASs), and intelligent resource management under dynamic network conditions. We discuss the state-of-the-art privacy-preserving techniques, such as federated learning, differential privacy, and secure aggregation, that can effectively protect sensitive user and device data during collective learning processes. The benefits and applications of edge intelligence for the purpose of providing intelligent security decision-making in real time and reducing the communication overhead are discussed. Furthermore, cross-layer optimization solutions for security, latency, energy efficiency, and performance trade-offs are presented. Lastly, the key limitations and open research challenges of current intelligent security mechanisms, such as scalability, complexity, and the balance between security and privacy, are highlighted. Our analysis can serve as a guidance and source of inspiration for future research in 6G intelligent security.