What question did this study set out to answer?

This study aims to systematically review machine-learning techniques for detecting DDoS attacks within Software-Defined Networks.

February 21, 2026Future InternetOpen Access

A Systematic Review of Machine-Learning-Based Detection of DDoS Attacks in Software-Defined Networks

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This study aims to systematically review machine-learning techniques for detecting DDoS attacks within Software-Defined Networks.
Conducted a PRISMA-guided systematic literature review.
Analyzed 38 primary studies from various digital libraries published between 2021 and 2025.
Examined learning paradigms, evaluation metrics, and datasets used in DDoS detection.
Assessed the integration of SDN emulation platforms like Mininet with controllers.
Single machine-learning classifiers dominate, but hybrid and ensemble methods are on the rise.
Performance metrics include accuracy, precision, recall, and F1 score, alongside system-level metrics like detection latency.
Several studies use public datasets, yet many rely on custom datasets specific to SDN.
Challenges persist in generalizing results for low-rate and unknown attacks and validating under real-time conditions.

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Implication

Systematic review identifies trends in machine-learning detection of DDoS attacks in software-defined networks, suggesting improvements in security measures.

Cite This Study

Ganeshan et al. (2026) studied this question.

synapsesocial.com/papers/69994c4b873532290d020a36 https://doi.org/10.3390/fi18020109

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Implication

Systematic review identifies trends in machine-learning detection of DDoS attacks in software-defined networks, suggesting improvements in security measures.

A Systematic Review of Machine-Learning-Based Detection of DDoS Attacks in Software-Defined Networks

Key Points

Discussion

Implication

Cite This Study

A Systematic Review of Machine-Learning-Based Detection of DDoS Attacks in Software-Defined Networks

Key Points

Discussion

Implication

Cite This Study

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