AI-Based Cognitive Twin Models for Software-Defined IoT Security and Analytics

The rise of Software-Defined Internet of Things (SD-IoT) systems has introduced new challenges in managing security and analytics due to the complexity and heterogeneity of connected devices. Traditional methods struggle to adapt in real-time to evolving threats and performance requirements. This paper presents an AI-Based Cognitive Twin Model tailored for SD-IoT environments, designed to enhance both security and analytics capabilities. By creating a digital replica of the IoT ecosystem, the cognitive twin continuously monitors, analyzes, and adapts to system changes. Leveraging machine learning and anomaly detection, it proactively identifies security risks and optimizes system performance through real-time analytics. Experimental results demonstrate that the cognitive twin model achieves a 35% improvement in threat detection accuracy and reduces false positive rates by 20% compared to traditional rule-based approaches. Additionally, the system's adaptability results in a 25% increase in response speed to emerging threats and a 30% improvement in predictive maintenance scheduling accuracy, minimizing device downtime. This AI-powered cognitive twin model offers a robust and adaptive solution for enhancing security and analytics in SD-IoT systems, making it suitable for dynamic environments requiring resilient and scalable monitoring frameworks.