ONTOLOGY-ENABLED DIGITAL TWIN DESIGN WITH AI-BASED DATA MANAGEMENT AND PRIVACYPRESERVING MECHANISMS FOR SECURE 6G COMMUNICATION SYSTEMS

Sixth generation (6G) communication networks are anticipated to facilitate the achievement of ultra-low latency, massive device connections, intelligent automation, and high-security in the end-to-end connectivity to accommodate new applications, including autonomous systems, immersive communications, and massive infrastructures of cyber-physical uses. In this regard, Digital Twin (DT) technology has experienced a lot of interest to present real-time virtual copies of the physical entities in the network, where predictive analysis, pre-emptive optimization, and self-managed network management can be provided. Nonetheless, the current DT-based wireless network frameworks have shortcomings in semantic interoperability, scalability, and data management, which do not provide much privacy protection in the highly distributed space. To overcome these drawbacks, this paper suggests introducing an ontology-based digital twin framework that is combined with AI-based data management and privacy protection tools that could be implemented to support the implementation of secure 6G communication systems. The offered framework uses domain-specific semantic ontologies to formally describe 6G network components, services, and security policies on the basis of which knowledge interoperability and context-aware reasoning could be ensured among heterogeneous network layers. Algorithms based on powerful machine learning are integrated in order to achieve intelligent prediction of traffic, adaptable resource distribution, anomaly detection, and a self-regulating system of network controls in the digital twin setting. Moreover, privacy-sensitive technologies, such as federated learning, differential privacy, and secure multi-party computation, are also integrated to secure delicate network information and ensure reliable AI activities. The proposed solution shows that the traffic prediction accuracy is represented by R 2 of 0.76, and the path coefficients of the proposed AI-driven network transformation and privacy protection efficacy are 0.45 (p < 0.001) and 0.38 (p < 0.001), respectively. Network resilience has an explained variance (R 2) of 0.72, which implies that the model fits well. An elaborate workflow model and system architecture are provided, and the performance and security analysis is done. The findings reveal that the suggested solution is highly effective to advance network intelligence, enhance privacy protection, and increase the resilience to cyber threats, and thus can be discussed as a powerful and scalable solution to achieve secure, intelligent, and autonomous network ecosystems of 6G.

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