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Professor, Head, Department of Artificial Intelligence and Data Science, Nandha Engineering College , Tamil Nadu , India
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Associate Professor, College of Engineering and Applied Sciences, American University of Kuwait , Salmiya , Kuwait
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Department of Preschool and Primary Education, Termez University of Economics and Service , Termez , Uzbekistan
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Assistant Professor, Department of Computer Science and Engineering, Nandha College of Technology , Erode , India
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Assistant Professor, Department of Electronics and Communication Engineering, Kangeyam Institute of Technology (Autonomous) , Tiruppur , India
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Assistant professor, Department of computer science and design, Kongu Engineering College , Erode , India
Associate Professor, Department of Electronics and Communication Engineering, Nandha Engineering College , Erode , India
The 6G wireless networks will be used in autonomous systems, extended reality, digital healthcare, and large-scale cyber-physical infrastructures, making it possible to provide applications they previously did not know to be intelligent and ultra-reliable communication. Nevertheless, 6G networks are quite complicated and heterogeneous, which are challenging to Security, resilience, and autonomous management and cannot be addressed successfully with common reactive techniques. In an attempt to solve these issues, this paper presents an AI-based Digital Twin architecture, which is characterized by ontology-based knowledge representation, intelligent data management, and simulation-based analysis to enhance Security and resilience. The suggested structure forms an ever-in-step virtual representation of the real 6G network that will allow real-time tracking, predictive analytics, and proactive decision-making. Integrating AI models with the framework helps identify intrusions and detect anomalies at 98 percent accuracy, and also shortens the response time by 40 percent. Also, the multi-level simulated environment analyzes the cyber-attack cases and action plans prior to deployment, making the system resilient, with the recovery time of failures cut by 30 percent. This is a closed-loop automation that is driven by statistical learning and rational decision-making based on knowledge, resulting in higher situational awareness, shorter response time, and higher network availability. The continual learning ability of the Digital Twin that keeps models updated with live network information results in its flexibility to the dynamic and heterogeneous nature of the future 6G networks. The solution provides a scalable, secure, and resilient base of autonomous 6G networks, which can help achieve the creation of trustworthy and intelligent communication systems.
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