Cross-Layer Security for 5G/6G Network Slices: An SDN, NFV, and AI-Based Hybrid Framework

Area of Science:

• Telecommunications Engineering
• Network Security
• Artificial Intelligence

Background:

• Fifth-generation (5G) and sixth-generation (6G) networks utilize network slicing for flexible resource allocation.
• Traditional security measures are insufficient for the dynamic and complex nature of sliced networks.
• Emerging network architectures present novel security vulnerabilities.

Purpose of the Study:

• To propose and evaluate a Hybrid Security Framework for securing network slices in 5G/6G environments.
• To leverage cross-layer integration of Software-Defined Networking (SDN) and Network Function Virtualization (NFV) with AI.
• To enhance the integrity and confidentiality of network slices against evolving threats.

Main Methods:

• Developed a Hybrid Security Framework integrating SDN, NFV, and AI-driven anomaly detection.
• Implemented and tested the AI-based anomaly detection component using a 5G security dataset.
• Focused on cross-layer security integration for comprehensive threat mitigation.

Main Results:

• The AI-driven anomaly detection demonstrated promising accuracy in identifying threats.
• The framework showed real-time response capabilities for detected security incidents.
• Initial implementation confirmed the feasibility of AI-based security in sliced networks.

Conclusions:

• The proposed Hybrid Security Framework offers a scalable and intelligent approach to network security.
• Cross-layer integration is crucial for addressing security challenges in dynamic 5G/6G environments.
• Further development of full SDN/NFV integration is recommended for robust next-generation network defenses.