Artificial Intelligence and Quantum Synergies in Trust-Enhanced Consumer Applications for Software Defined Networks

Software Defined Networks (SDN) have revolutionized multimedia communication systems with their dynamic resource allocation and load balancing capabilities. However, ensuring security and trust within these networks poses significant challenges which is the aim of this research to improve SDN security and efficiency in consumer applications. The proposed approach integrates Ubiquitous AI, Machine Learning (ML), and Quantum Computing with Trust Management principles. We propose an ML model, which takes inspiration from Quantum Computing to ensure the robustness of network security. At the time we have developed an AI driven SDN architecture to enhance its usage. Additionally, we have introduced a Trust Management protocol that verifies the reliability and trustworthiness of both service providers and network nodes. To evaluate the performance of our model, we conducted simulations in environments with different attack scenarios. The results showed that our model achieved a detection accuracy of 98.5% for passive eavesdropping attacks, 95% for Sybil attacks, and 92% for coordinated attacks, while maintaining reasonable computational costs.