<![CDATA[Software-Defined Networking (SDN) has become a key enabler of next-generation communication infrastructures because of its centralized control, programmability, and global network visibility. However, the centralized architecture also introduces significant security vulnerabilities, making SDN environments highly susceptible to attacks such as DoS, DDoS, probing, brute-force, and botnet activities. Although deep learning-based intrusion detection systems have achieved high detection accuracy, many existing approaches suffer from high computational complexity, long training time, and limited suitability for real-time deployment. This study addresses this gap by developing a lightweight stacking ensemble intrusion detection framework for SDN using the InSDN dataset. The proposed framework employs XGBoost, LightGBM, CatBoost, Random Forest, and Extra Trees as base learners, with Logistic Regression serving as the meta-learner. Experiments were conducted using 48-feature, 6-feature, and 4-feature configurations derived from previous feature-reduction studies. The results demonstrate consistently high detection performance, achieving accuracies above 99% across all feature subsets, with only marginal degradation under reduced feature dimensions. The framework showed excellent detection capability for major attack categories while maintaining reliable performance for most minority classes. These findings demonstrate that stacking ensemble learning is a practical and computationally efficient alternative to complex deep learning architectures for SDN intrusion detection, with strong potential for scalable and real-time cybersecurity deployment in modern network environments.]]>
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