Data-Driven Security Frameworks: AI-Infused Digital Twin Software Solutions for IoT
B. Sobhan Babu, Arokia Suresh Kumar Joseph, J. Premalatha, Mohammed Alisha, Nalini Chekuri, Vinnarasi Saravanan · International Journal of Computational and Experimental Science and Engineering · 2025
The rapid proliferation of Internet of Things (IoT) devices has introduced new challenges in maintaining the security and integrity of interconnected systems. Traditional security models struggle to keep pace with the dynamic and complex nature of IoT environments, leading to vulnerabilities and threats that are difficult to detect and mitigate in real-time. This paper presents a Data-Driven Security Framework named AI-Infused Digital Twin Software Solution (AI-DTSS), designed specifically for IoT environments. The proposed framework continuously monitors IoT networks by creating a digital replica of each device, capturing real-time data streams, and analyzing them using an ensemble of AI models, including recurrent neural networks (RNNs) for sequence prediction and generative adversarial networks (GANs) for synthetic data generation. An adaptive threat response mechanism is implemented to automatically update security protocols based on detected anomalies. The proposed AI-Infused Digital Twin Software Solution provides a scalable, robust, and adaptive security framework for IoT networks. By leveraging digital twin technology in conjunction with AI models, AI-DTSS offers a real-time, data-driven approach to threat detection and response, making it a valuable tool for securing complex IoT ecosystems.