AI-Powered Predictive Digital Twin Platforms for Secure Software-Defined IoT Networks

The rapid evolution of Internet of Things (IoT) networks has led to an increasing reliance on Software-Defined Networking (SDN) to address the complexities of network management and resource allocation. However, the security challenges in IoT networks remain a major concern due to the diversity of devices, heterogeneity of communication protocols, and vulnerabilities to cyber-attacks. This paper proposes an AI-powered predictive Digital Twin (DT) platform integrated into software-defined IoT networks for enhanced security and performance optimization. The platform uses machine learning models to create real-time digital replicas of IoT devices, which are employed to simulate and predict network behaviors, identify potential security threats, and optimize network traffic flow. Through the integration of predictive analytics and anomaly detection techniques, the proposed system provides proactive defense mechanisms against security breaches, improves fault tolerance, and enhances resource utilization. Experimental results demonstrate the effectiveness of the platform in improving the resilience of IoT networks against attacks and optimizing their operational efficiency, thereby contributing to more secure and scalable IoT systems