Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Search research articles

Related Experiment Videos

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

Khadija Kanwal¹, Muhammad Mujahid², Julio Cesar Martinez Espinosa^3,4,5,6

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

Scientific Reports

|June 24, 2026

Summary

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

A framework based on hybrid Suzuki-Abe and convex Hull approach for improved classification of skin lesions.

Scientific reports·2026

Same author

XAI-MedNet: A Next-Generation Explainable AI Framework for Contrast-Enhanced Skin Lesion Classification via Entropy-Controlled Optimization.

Bioengineering (Basel, Switzerland)·2026

Same author

Heart rate optimizer: a novel bio-inspired metaheuristic algorithm.

Scientific reports·2026

Same author

An Integrated Machine Learning and Genomic Framework for Precise Detection of Gastric Cancer.

The American journal of pathology·2026

Same author

Improving Deep Learning Based Lung Nodule Classification Through Optimized Adaptive Intensity Correction.

Bioengineering (Basel, Switzerland)·2026

Same author

An attention-based deep learning model for early detection of polyphagous shot hole borer infestations in plants.

BMC plant biology·2026

Same journal

MT-MRI for detection of renal interstitial fibrosis in renovascular disease.

Scientific reports·2026

Same journal

Detection of underground objects from GPR data using a lightweight YOLO-based approach.

Scientific reports·2026

Same journal

Early systemic inflammatory-metabolic trajectory phenotypes are associated with survival outcomes in metastatic renal cell carcinoma treated with nivolumab.

Scientific reports·2026

Same journal

Water balance components in a dry-seeded rice-wheat system: Untangling the effects of tillage and mulching practices.

Scientific reports·2026

Same journal

Topological approaches to quantum tensor train compression via ZX-calculus and SVD.

Scientific reports·2026

Same journal

determinants of flood impacts and adaptive capacity among market vendors in Walukuba-Masese, Jinja city, Uganda.

Scientific reports·2026

See all related articles

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.

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.

Keywords:

Feature selection Hybrid model Machine learning Network intrusion detection Network security Software defined networks

Related Experiment Videos

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.