Hybrid Digital Twin Solutions for Real-Time Threat Prevention in AI-Driven IoT Networks - R. Thiyagarajan; D.Mohana Geetha; Ahmed Mudassar Ali; K Sreekanth (International Journal of Computational and Experimental Science and Engineering, 2025)
The integration of Digital Twin (DT) technology with Artificial Intelligence (AI) has shown significant promise in enhancing the security and operational efficiency of Internet of Things (IoT) networks. This paper proposes a Hybrid Digital Twin solution designed for real-time threat prevention in AI-driven IoT networks. By leveraging AI-driven decision-making processes, coupled with the real-time simulation and monitoring capabilities of Digital Twins, the proposed framework continuously analyzes IoT network behaviors and predicts potential security threats. The hybrid approach combines machine learning (ML) models with Digital Twin simulations to predict network vulnerabilities and detect anomalous behaviors at an early stage, thereby preventing security breaches before they impact the system. The architecture includes a real-time monitoring system for both physical and virtual assets, providing insights into the IoT network's current state and enabling proactive threat mitigation. Experimental results demonstrate a 15% reduction in false-positive threat detection, a 20% improvement in response time to potential threats, and a 17% increase in overall network efficiency when compared to conventional threat prevention methods. The proposed framework integrates the following components, Real-time data acquisition from IoT devices and systems, AI-based anomaly detection algorithms for threat identification, Digital Twin simulation models for continuous network status monitoring and predictive analytics. Automated response mechanisms based on AI predictions and Digital Twin assessments. The effectiveness of the proposed solution is validated through case studies and performance evaluations, highlighting its ability to enhance the security, reliability, and efficiency of AI-driven IoT networks. Future work will focus on improving the scalability of the solution, optimizing resource allocation, and extending its application to more diverse IoT environments.