6G-Enabled Digital Twin–Driven Predictive Security Framework for University Research Management and Big Data Protection

An AI-Based Digital Twin Framework for Secure and Intelligent Cyber-Physical Systems

ABSTRACT The rapid growth of Industry 5.0, smart cities, healthcare, and intelligent transportation has increased the need for secure and efficient cyber-physical systems (CPS). Traditional AI-based systems often face challenges such as security risks, high communication delays, and limited scalability. This paper presents an AI-based Digital Twin Framework that combines Digital Twin technology, Artificial Intelligence (AI), Federated Learning (FL), Blockchain, Edge Computing, and Explainable AI (XAI) to improve system performance and security. The proposed framework continuously collects data from IoT sensors, creates a virtual model of physical devices, and uses deep learning algorithms for real-time monitoring, fault detection, and predictive analysis. Federated learning protects user privacy by training AI models without sharing raw data, while blockchain ensures secure and tamper-proof data storage. Edge computing reduces latency by processing data closer to the source, enabling faster decision-making. The proposed system improves accuracy, reliability, security, and scalability while reducing response time and communication overhead. It can be applied in smart manufacturing, healthcare, transportation, renewable energy, and other intelligent applications. Overall, the framework provides an efficient, secure, and intelligent solution for next-generation cyber-physical systems. Keywords— Artificial Intelligence, Digital Twin, Federated Learning, Blockchain, Edge Computing, Explainable AI, Internet of Things (IoT), Cyber-Physical Systems, Smart Manufacturing, Deep Learning.

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