Enhancing IoT Fog Computing Security: A Dynamic Nonlinear Analytical Model-Based SDN Assessment Framework

Abstract

In the rapidly evolving landscape of the Internet of Things (IoT) and fog computing, securing distributed networks has become a critical challenge. The integration of IoT devices into various sectors, including industrial systems and everyday applications, exposes these environments to a wide range of dynamic security threats. This article introduces a novel security assessment framework titled "Enhancing IoT Fog Computing Security: A Dynamic Nonlinear Analytical Model-Based SDN Assessment Framework," which leverages Software Defined Networking (SDN) to enhance the security posture of IoT and fog computing systems. The framework employs nonlinear analytical models to dynamically optimize network configurations, enhance data privacy, and automate threat response mechanisms across IoT, fog, and cloud layers. It integrates distributed SDN controllers to manage resources, enforce security policies, and facilitate real-time monitoring and anomaly detection. Emphasizing localized security assessments at fog nodes, the framework reduces latency and offloads the cloud layer, ensuring rapid response to emerging threats. The nonlinear models underpin algorithms that adapt to changing network conditions, balancing security with operational efficiency. This dual focus enables the framework to address scalability, device integration, and real-time threat management. By adopting a dynamic and nonlinear approach, the framework provides a robust, adaptable solution to the complex security challenges in IoT and fog computing, fostering secure, efficient, and resilient network environments.