## 2 Architectures of 5G and 6G Networks
The city deployed a network of edge servers to manage traffic lights and pedestrian safety systems in real time. Although the architecture delivered near-instantaneous responses, a security assessment revealed potential breaches through unpatched edge servers. In response to such threats, the city's Information Technology department deployed an orchestration platform that ensures consistent monitoring of server software versions and machine learning-driven anomaly detection systems at each edge node. This case just goes to show the critical need for constant patch management, device attestation, and thorough threat detection across all edge locations. Therefore, despite the benefits of reduced latency and improved localized threat response, edge computing introduces vulnerabilities due to its distributed, resource-constrained nature. Challenges include inconsistent security enforcement, patching difficulties, and managing security across dispersed nodes. A Comprehensive Security Framework for 6G Networks and Softwarized InfrastructuresLooking ahead to 6G and more advanced network paradigms, the trend toward highly distributed, dynamic, and softwarized infrastructures will intensify [19]. Consequently, novel security frameworks are essential to address emerging threats in these evolving environments. Figure 2 illustrates our proposed security framework drivers for 6G networks encompassing key characteristics for enhanced security. More specifically, our framework includes the above key drivers:  
* **Network Slicing and Micro-Slicing:** Building upon foundational 5G concepts, 6G introduces more granular network slicing--dynamically allocate network resources in smaller, context-specific segments--enabling the creation of highly specialized virtual networks tailored for specific applications and services, each with unique performance. This includes micro-slicing, which allows for dynamic and fine-grained resource allocation to meet diverse performance, security, and isolation requirements. * **AI/ML Integration for Self-Organizing Networks (SONs):** Artificial intelligence (AI) and machine learning (ML) are central to 6G's SONs, facilitating real-time analysis of network conditions. These technologies enable proactive fault management, dynamic resource orchestration, and rapid threat detection, ensuring optimal network performance and resilience. More specifically, machine learning algorithms greatly enhance network security through adaptive, real-time anomaly detection, capable of identifying previously unseen threats. However, these techniques are not without limitations, notably requiring extensive computational resources, potential susceptibility to adversarial attacks, and issues around model transparency and explainability, particularly relevant under the EU AI Act. Reinforcement learning enables dynamic threat mitigation strategies, adapting to evolving security landscapes. However, it faces challenges such as slow convergence times and the inherent exploration-exploitation trade-off that could delay effective threat response. * **Quantum-Safe Protocols and Encryption:** As computing power grows, securing data against emerging quantum attacks becomes a priority, driving research into post-quantum cryptography [20]. To counter this, future 6G networks have to adopt post-quantum cryptographic algorithms, such as those standardized by NIST in 2024, including CRYSTALS-Kyber and CRYSTALS-Dilithium [21] to ensure long-term data security. Despite offering strong protection against quantum attacks, these protocols face practical challenges such as computational overhead, larger key sizes, and implementation complexity, especially in resource-constrained environments like IoT and edge computing. * **Ubiquitous Sensors and Massive IoT:** The proliferation of IoT devices both in current 5G and emerging 6G networks [22] amplifies data traffic and widens the attack surface.