AI‐Driven Anomaly Detection and Traffic Management in Software‐Defined IoT Networks for Smart Agriculture

ABSTRACT Smart agriculture uses Internet of Things (IoT) and sensors to increase productivity, optimize resource use, and enhance decision‐making. However, the large and varied agricultural data sets pose challenges for traffic management and network security. Traditional software‐defined networking (SDN) offers centralized, flexible traffic control, but it cannot detect malicious or abnormal activities in real‐time. To fill this gap, we introduce an Artificial Intelligence (AI)‐based anomaly detection and traffic management framework for SD‐IoT networks in smart agriculture. This system combines a machine learning (ML) Intrusion Detection System (IDS) with the SDN controller, enabling real‐time identification of abnormal traffic and adaptive prioritization of critical data flows. The IDS detects unusual traffic patterns, while the SDN controller allocates resources and routes flows based on quality of service (QoS) and security needs. Simulations demonstrate that our system achieves high accuracy in anomaly detection, reduces latency for emergency flows, enhances throughput, and reduces packet loss compared to traditional methods. This work highlights the importance of integrating AI‐powered IDS with SDN traffic management to enhance security and communication in smart agricultural networks.