Software-Defined Networking (SDN) has revolutionized network management by decoupling the control and data planes, offering enhanced flexibility and programmability. However, this shift has also introduced significant security challenges, particularly for traditional Intrusion Detection Systems (IDS), which often struggle to adapt to SDN’s dynamic nature. To address these challenges, this paper proposes a Deep Hybrid IDS model that integrates both signature-based and anomaly-based detection techniques, leveraging deep learning models, including Gated Recurrent Units (GRU), Long Short-Term Memory (LSTM), and deep neural networks (DNN). The model is further optimized through feature selection, data normalization, and augmentation to improve detection accuracy and efficiency. Strategies such as hyperparameter tuning, attention mechanisms, ensemble learning, transfer learning, and cross-validation ensure robust model training and adaptability. Additionally, adversarial training and meta-learning techniques are incorporated to enhance resilience against evolving threats. A real-time feedback loop is implemented to monitor the system’s performance, ensuring scalability and adaptability during deployment. This hybrid approach not only enhances the detection capabilities of IDS in SDN environments but also strengthens the overall security posture of modern network infrastructures.