A Novel Multiple Role Evaluation Fusion-Based Trust Management Framework in Blockchain-Enabled 6G Network.

Area of Science:

• Computer Science
• Network Security
• Telecommunications Engineering

Background:

• Six Generation (6G) networks face security challenges due to increased device density and complexity.
• Secure communication and collaboration rely heavily on robust trust evaluation mechanisms.
• Existing trust models may not adequately address the unique vulnerabilities of 6G environments.

Purpose of the Study:

• To propose a novel multiple role fusion trust evaluation framework for 6G networks.
• To enhance the security and reliability of 6G communication systems.
• To improve trust prediction and anomaly detection capabilities.

Main Methods:

• Integration of multiple role fusion trust calculation with blockchain-based trust management.
• Utilization of a neural network fitting method for fused trust value prediction.
• Optimization of traditional trust management frameworks for 6G applications.

Main Results:

• The proposed multiple role fusion approach demonstrates superior stability compared to single-role evaluation.
• Enhanced performance in anomaly detection within the 6G network environment.
• Improved overall evaluation accuracy for node trust.

Conclusions:

• The multiple role fusion trust evaluation framework offers a more secure and stable solution for 6G networks.
• Blockchain integration provides a robust foundation for trust management in decentralized 6G systems.
• The optimized model significantly enhances the security of communication systems through accurate trust prediction.