AI-Enhanced Virtual Twin Modelling for Strengthening IoT Software Security Protocols

To fortify security mechanisms in software systems for the Internet of Things (IoT), this article presents a framework for AI-Enhanced Virtual Twin Modelling. In order to track and examine the actions of IoT devices in real-time, the suggested method makes use of virtual twin technology that is combined with machine learning techniques. Automated response generation, continuous threat assessment, and anomaly detection are made possible by creating a digital clone of the actual IoT network using the virtual twin paradigm. By combining deep learning models like CNNs and LSTMs, it becomes easier to forecast possible security risks in network traffic data by seeing intricate spatial and temporal patterns. When tested in a virtual Internet of Things (IoT) setting, the suggested framework outperforms conventional rule-based approaches by a wide margin, reducing reaction time by 35% while achieving an accuracy of $\mathbf{9 6 . 2 \%}$ in threat identification.