Enhancing Security in Digital Twin-Based Smart IoT Environments
Keywords:
Artificial Intelligence (AI), Blockchain technology, Digital twin security, Edge computing, Internet of Things (IoT)Abstract
Digital Twin (DT) technology has emerged as a key enabler of smart Internet of Things (IoT) systems by providing real-time monitoring, predictive analytics, and intelligent decision-making capabilities. However, the integration of DT with IoT introduces significant security challenges due to distributed architectures, continuous data exchange, and heterogeneous devices. This paper presents a comprehensive study of security issues in DT-enabled IoT systems, including threat models across device, network, data, application, and DT layers. To address these challenges, a hybrid security framework integrating Artificial Intelligence (AI), Blockchain, and Edge Computing is proposed. The framework leverages AI-based intrusion detection for real-time anomaly identification, blockchain for secure and tamper-proof data validation, and edge computing for low-latency processing. Mathematical models are developed to evaluate threat detection, intrusion probability, and trust mechanisms. Experimental results demonstrate that the proposed hybrid approach achieves 96% detection accuracy, significantly reduces false alarms, and lowers latency compared to traditional methods. The study highlights the effectiveness of combining AI and blockchain in enhancing security resilience and real-time responsiveness. This work contributes toward developing a unified, scalable, and secure Digital Twin framework suitable for next-generation smart environments such as smart cities, healthcare IoT, and industrial systems.
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