Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

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

HLD-DDoSDN: High and low-rates dataset-based DDoS attacks against SDN.

PloS one·2024
Same author

Meta-Learner-Based Approach for Detecting Attacks on Internet of Things Networks.

Sensors (Basel, Switzerland)·2023
Same author

Approach for Detecting Attacks on IoT Networks Based on Ensemble Feature Selection and Deep Learning Models.

Sensors (Basel, Switzerland)·2023
Same author

CNN-CNN: Dual Convolutional Neural Network Approach for Feature Selection and Attack Detection on Internet of Things Networks.

Sensors (Basel, Switzerland)·2023
Same author

Conditional Tabular Generative Adversarial Based Intrusion Detection System for Detecting Ddos and Dos Attacks on the Internet of Things Networks.

Sensors (Basel, Switzerland)·2023
Same author

Efficient Authentication Scheme for 5G-Enabled Vehicular Networks Using Fog Computing.

Sensors (Basel, Switzerland)·2023
Same journal

RETRACTED: Zhang et al. A Novel Framework for Reconstruction and Imaging of Target Scattering Centers via Wide-Angle Incidence in Radar Networks. <i>Sensors</i> 2025, <i>25</i>, 6802.

Sensors (Basel, Switzerland)·2026
Same journal

Enhancing Unsupervised Multi-Source Domain Adaptation for Person Re-Identification via Mixture of Experts and Graph-Based Relation.

Sensors (Basel, Switzerland)·2026
Same journal

Development of an Instrumented Glove for Palmar Pressure Assessment in Kayakers.

Sensors (Basel, Switzerland)·2026
Same journal

Development and Experimental Validation of an Autonomous IoT-Based Monitoring System for Real-Time Water Quality Assessment in the Amazon River.

Sensors (Basel, Switzerland)·2026
Same journal

Semi-Supervised Adversarial Learning Framework for Controller Area Network Bus Intrusion Detection.

Sensors (Basel, Switzerland)·2026
Same journal

Smart Optimization Method for Safety Signs in Innovative Manufacturing Environments Integrating Industrial Field IoT Sensors and Knowledge Graphs.

Sensors (Basel, Switzerland)·2026
See all related articles

Related Experiment Video

Updated: Jul 30, 2025

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning
04:17

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning

Published on: May 10, 2024

829

A Systematic Literature Review on Machine Learning and Deep Learning Approaches for Detecting DDoS Attacks in

Abdullah Ahmed Bahashwan1, Mohammed Anbar1, Selvakumar Manickam1

  • 1National Advanced IPv6 Centre (NAv6), Universiti Sains Malaysia, Gelugor 11800, Penang, Malaysia.

Sensors (Basel, Switzerland)
|May 13, 2023
PubMed
Summary
This summary is machine-generated.

This review analyzes machine learning and deep learning methods for detecting distributed denial of service (DDoS) attacks in software-defined networking (SDN). Research shows a rise in these methods, but dataset limitations persist.

Keywords:
deep learning (DL)distributed denial of service (DDoS)intrusion detection system (IDS)machine learning (ML)software-defined networking (SDN)systematic literature review (SLR)

More Related Videos

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
08:20

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

1.5K
Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

591

Related Experiment Videos

Last Updated: Jul 30, 2025

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning
04:17

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning

Published on: May 10, 2024

829
Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
08:20

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

1.5K
Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

591

Area of Science:

  • Computer Science
  • Network Security
  • Artificial Intelligence

Background:

  • Software-defined networking (SDN) offers flexibility but is vulnerable to distributed denial of service (DDoS) attacks.
  • Existing DDoS detection methods in SDN face significant challenges, remaining an open research area.
  • Machine learning (ML) and deep learning (DL) show promise for enhancing SDN security against sophisticated threats.

Purpose of the Study:

  • To systematically review and analyze ML, DL, and hybrid approaches for DDoS attack detection in SDN.
  • To identify trends, common methodologies, and prevalent datasets used in SDN DDoS detection research.
  • To highlight existing challenges and open issues in the field.

Main Methods:

  • A systematic literature review (SLR) protocol was followed, involving automatic and manual searches across eight databases.
  • The review covered studies published between 2014 and 2022.
  • Seventy primary studies were identified and analyzed.

Main Results:

  • A significant increase in research on SDN DDoS detection using ML/DL approaches was observed in recent years.
  • Ensemble, hybrid, and single ML-DL models are the predominant methods employed.
  • Private synthetic and unrealistic datasets are commonly used for evaluating detection approaches.

Conclusions:

  • The field of SDN DDoS detection using ML/DL is rapidly evolving.
  • Current evaluation practices, particularly dataset usage, require improvement for more robust and realistic assessments.
  • Further research is needed to address identified challenges and enhance the effectiveness of SDN security.