Enhancing Threat Detection Accuracy in IIoT Networks using AI-based Models

Abstract

Software-Defined Networking (SDN) and Artificial Intelligence (AI) have the potential to address challenges in Industrial Internet of Things (IIoT) networks, such as security, scalability, and network performance, and this paper proposes a robust framework that leverages SDN and AI to enhance network management, real-time monitoring, and efficient resource utilization. SDN decouples the control and data planes, providing centralized control and simplifying the management because AI techniques, including machine learning and reinforcement learning, enable intelligent threat detection, optimized routing, and data protection. The synergistic fusion of these technologies ensures timely and secure data transmission, reduces latency and prevents data loss. Thus, the proposed framework enhances IIoT deployments through automation, predictive analytics, and proactive threat detection and mitigation. This paper demonstrates the integration of SDN and AI to create secure, scalable, and seamless IIoT networks that meet the requirements of modern industrial operations and information security standards. Therefore, it highlights the potential of SDN and AI to revolutionize IIoT networks, providing a comprehensive framework for future IIoT deployments.

Topics & Concepts