Research in SDN-Based Intrusion Detection and Prevention Systems (IDPS) is rapidly evolving to address the security challenges of dynamic and programmable networks. Prominent research areas include the design of machine learning and deep learning-based models for real-time attack detection, development of lightweight and scalable intrusion prevention frameworks for high-speed SDN environments, and integration of federated learning for privacy-preserving threat analysis across distributed controllers. Other important topics involve the application of blockchain for secure information sharing, implementation of Zero Trust security architectures within SDN-based IDPS, and creation of context-aware and adaptive flow control mechanisms for mitigating emerging threats. Additional directions include optimizing controller placement for minimal latency, designing energy-efficient detection systems for edge and IoT-integrated SDN networks, and developing hybrid rule-based and AI-driven techniques for proactive defense. These research topics collectively aim to enhance the intelligence, resilience, and adaptability of next-generation SDN-based network security infrastructures.