Design and evaluation of a software-defined networking intrusion detection and prevention system using ensemble machine learning and hybrid feature selection

Olumhense Benedict Adoghe, Francis Emmanuel Kibuebu, Ejemen Comfort Ikpotokin, Emmanuel Dominic Ephraim

Abstract


Cyber threats in today's software-defined network infrastructures are growing at an exponential rate, thus sophisticated, adaptive security measures are required.  In order to enhance detection accuracy and computational efficiency, this study introduces a Network Intrusion Detection and Prevention System (NIDPS) that is ensemble-based and designed for Software-Defined Networking (SDN). The NIDPS makes use of a hybrid feature selection technique.  As part of its soft-voting ensemble architecture, the system incorporates XGBoost, Decision Tree, and Support Vector Machine, three machine learning classifiers.  To tackle the difficulties of dealing with high-dimensional network traffic data, feature selection is optimised using Correlation-Based methods and Recursive Feature Elimination (RFE).  Datasets obtained from simulated Denial-of-Service (DoS) attacks were used to evaluate the model, which was constructed and tested in a virtualised, emulated SDN environment using the SEED Internet Emulator.  All of the model variations demonstrated near-perfect detection ability (up to 100% accuracy) in the experiments, with the fastest predictions coming from RFE-enhanced models.  The system is well-suited for implementation in actual programmable network settings due to its real-time alerting and preventive features.  The results of this study show that an effective and scalable method for intrusion detection in SDNs may be achieved by combining ensemble learning with intelligent feature selection.

 

Received: 29 November 2025

Accepted: 23 January 2026

Published: 22 April 2026


Keywords


Network Intrusion Detection and Prevention System; Ensemble Machine Learning; Hybrid Feature Selection; Software-Defined Networking; Denial-of-Service Attack; XGBoost; Recursive Feature Elimination; Cybersecurity; DoS Detection; SEED Internet Emulator

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References


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DOI: https://dx.doi.org/10.21622/ACE.2026.06.1.1834

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Copyright (c) 2026 Olumhense Benedict Adoghe, Francis Emmanuel Kibuebu, Ejemen Comfort Ikpotokin, Emmanuel Dominic Ephraim

Advances in Computing and Engineering

E-ISSN: 2735-5985

P-ISSN: 2735-5977

 

Published by:

Academy Publishing Center (APC)

Arab Academy for Science, Technology and Maritime Transport (AASTMT)

Alexandria, Egypt

ace@aast.edu