Using Artificial Intelligence and SDN for Dynamic Scalable Control of Security Rules: An IoT Security Solution

Abstract

Introduction The widespread uptake of the Internet of Things (IoT) has brought forth numerous security concerns as IoT environments become more heterogeneous, larger, and complex.Because of the evolving threats and large-scale management needed for IoT, traditional security models are often ill-equipped to keep up. In this context, researchers have started exploring the integration of Artificial Intelligence (AI) and Software-Defined Networking as an alternative solution for adapting and scaling IoT security management. In this article, we take a look at how both technologies are used together and contribute to increasing security for IoT. It starts by describing the major challenges and issues that are seen in securing IoT systems, like device heterogeneity, mass scale updating software security patches etc., visibility and control. It next explores the benefits of AI and SDN separately, describing how security rule settings can be automated using an AI-based method to enhance threat detection (through better performance prediction) and provide intelligent resource orchestration with respect to IoT networks as well as centralized programming by employing a single control plane that seamlessly scales for data processing. The article also went on to explain the promise of AI-SDN hybrid solutions in which data from both technologies is correlated creating a comprehensive IoT security environment that can power automated decision making and quick application of security policies. Apart from that, it documents some relevant case studies and also provides several practical insights into enforcing AI/SDN-driven IoT security solutions with the help of existing infrastructure in tandem with discussing future trends hitting cybersecurity perimeter. The presented work reveals that integration of AI and SDN has the vast capability to combat security issues in IoT environment.