Abdussalam Salama

Performance and Structural Symmetry Evaluation of Machine Learning-Driven Intrusion Detection Systems in Software-Defined Networks

This paper evaluates an ultra-compact five-feature polling scheme (F1–F5) designed to preserve statistical symmetry between control-plane monitoring and telemetry overhead within a dynamic Mininet–Ryu testbed and reveals that tree-based ensembles consistently outperform deep learning approaches.