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  1. Home
  3. Lwhm: Lightweight Hybrid Classifier For Sdn-attack Detection Using Recursive Feature Elimination.
  1. Home
  3. Lwhm: Lightweight Hybrid Classifier For Sdn-attack Detection Using Recursive Feature Elimination.

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LwHM: lightweight hybrid classifier for SDN-attack detection using recursive feature elimination.

Khadija Kanwal1, Muhammad Mujahid2, Julio Cesar Martinez Espinosa3,4,5,6

  • 1Institute of Computer Science and Information Technology, The Women University Multan, Multan, Pakistan.

Scientific Reports
|June 24, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

Artificial intelligence enhances software-defined networking (SDN) security by improving data integrity and using feature selection. A hybrid AI model achieved 99.93% accuracy in detecting network intrusions.

Keywords:
Feature selectionHybrid modelMachine learningNetwork intrusion detectionNetwork securitySoftware defined networks

Related Experiment Videos

Area of Science:

  • Cybersecurity
  • Artificial Intelligence
  • Network Security

Background:

  • Increased reliance on internet connectivity necessitates robust cybersecurity measures.
  • Cybersecurity threats to data, privacy, and critical systems are prevalent.
  • Software-defined networking (SDN) requires advanced security solutions.

Purpose of the Study:

  • To investigate the role of artificial intelligence (AI) in enhancing SDN security.
  • To propose a comprehensive three-part approach for securing SDN environments.
  • To develop and evaluate a lightweight hybrid model (LwHM) for intrusion detection.

Main Methods:

  • Data integrity assurance through cleaning, preprocessing, and normalization of an SDN intrusion dataset.
  • Application of six feature selection strategies: RFE, polynomial features, ANNs, SelectKBest, LASSO, and correlation-based features.
  • Development of a lightweight hybrid model (LwHM) using k-nearest neighbors and decision trees with a voting classifier.

Main Results:

  • Feature selection techniques identified significant features, improving model performance.
  • The LwHM demonstrated superior performance on the InSDN dataset.
  • An accuracy score of 99.93% was achieved using Recursive Feature Elimination (RFE) features.

Conclusions:

  • The proposed AI-driven approach effectively enhances SDN security.
  • The LwHM offers an efficient solution for thwarting network breaches.
  • AI plays a crucial role in strengthening cybersecurity for modern networks.