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A hybrid machine learning approach for detecting DDoS attacks in software-defined networks.

Iftekhar Ahmed Mahar1, Kamran Aziz2, Prasun Chakrabarti3

  • 1School of Computer Science, Wuhan University, Wuhan, 430000, China.

Scientific Reports
|January 28, 2026
PubMed
Summary

This study introduces a machine learning framework for detecting Distributed Denial of Service (DDoS) attacks in Software-Defined Networking (SDN). A hybrid Random Forest-XGBoost model achieved 99.36% accuracy, offering reliable early detection for programmable networks.

Keywords:
Distributed denial of service (DDoS)Feature engineeringFlow statisticsHybrid classification modelMachine learningOpenFlowPort statisticsRandom forestSDN securitySoftware-defined networking (SDN)Traffic classificationXGBoost

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Area of Science:

  • Computer Science
  • Network Security
  • Machine Learning

Background:

  • Software-Defined Networking (SDN) offers programmability but introduces vulnerabilities to attacks like Distributed Denial of Service (DDoS).
  • Existing detection methods often lack specificity for SDN environments, necessitating SDN-aware traffic features.
  • OpenFlow-based networks require tailored approaches for effective threat identification.

Purpose of the Study:

  • To develop and evaluate a machine learning framework for early DDoS attack detection in SDN.
  • To engineer novel SDN-specific traffic features for improved threat identification.
  • To assess the performance of a hybrid Random Forest (RF) and XGBoost (XGB) classification model.

Main Methods:

  • Constructed a dataset from an SDN testbed using a Ryu controller and Open vSwitch.
  • Collected flow and port-level statistics via OpenFlow monitoring messages.
  • Engineered SDN-specific features and developed a hybrid RF-XGB classification model.

Main Results:

  • The hybrid RF-XGB model achieved 99.36% accuracy in distinguishing benign from malicious traffic.
  • Demonstrated superior performance compared to individual Random Forest and XGBoost classifiers.
  • Exhibited near-perfect discrimination in Receiver Operating Characteristic (ROC) Area Under the Curve (AUC) and confusion matrix evaluations.

Conclusions:

  • Combining SDN-specific feature engineering with ensemble learning (RF-XGB) is highly effective for early DDoS detection.
  • The proposed framework offers a reliable solution for enhancing security in programmable networks.
  • SDN-aware features are crucial for accurately identifying sophisticated network threats.