The increasing integration of IoT devices into modern infrastructures necessitates robust frameworks to secure data transmission and enhance network performance. This paper presents a secure Software-Defined Networking (SDN)-IoT framework that combines adaptive Gbell Probability-Based Fuzzy Rule Matching (Gbell PRF-MAC) and Convolutional GRU (CGRU) for Intrusion Detection Systems (IDS). The proposed framework demonstrated exceptional performance in addressing key challenges of data security and SDN layer efficiency. It employed Gbell PRF-MAC to create and validate adaptive Message Authentication Codes (MACs) with optimal timings of 1789ms for generation and 2234 ms for verification, ensuring robust validation while expediting user identification for secure SDN access. Simultaneously, IoT data transmission was safeguarded using adaptive encryption, achieving an impressive security level (SL) of 99.12%. For intrusion detection, the CGRU model achieved a remarkable accuracy of 99.86%, effectively distinguishing between attack and non-attack scenarios through optimized feature selection, which also minimized computational overhead. Additionally, the integration of SDN intelligence and IoT adaptability enabled dynamic Service Level Agreement (SLA) management, achieving a response time of 1449 ms and ensuring smooth and efficient service delivery. This synergy between advanced security mechanisms and SDN-IoT flexibility provides a robust, scalable, and adaptive solution for modern infrastructures. The proposed framework not only mitigates evolving cyber threats but also enhances data security and network efficiency, establishing a comprehensive approach to secure IoT-based ecosystems. This study demonstrates its potential to be a cornerstone for secure and efficient next-generation IoT implementations.