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A DDoS Detection Method Based on Feature Engineering and Machine Learning in Software-Defined Networks.

Zhenpeng Liu1,2, Yihang Wang1, Fan Feng2

  • 1School of Electronic Information Engineering, Hebei University, Baoding 071002, China.

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Summary
This summary is machine-generated.

This study introduces a machine learning method to detect distributed denial-of-service (DDoS) attacks in software-defined networks (SDNs). Random Forest achieved the best performance in identifying these significant cybersecurity threats.

Keywords:
DDoS attacksbinary grey wolf optimization algorithmfeature engineeringmachine learningsoftware-defined networking

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Area of Science:

  • Cybersecurity
  • Network Security
  • Machine Learning Applications

Background:

  • Distributed Denial-of-Service (DDoS) attacks present a critical threat to the stability and security of Software-Defined Networks (SDNs).
  • Effective detection mechanisms are crucial for maintaining the integrity and availability of SDN infrastructure.
  • Existing detection methods may require optimization for improved accuracy and efficiency.

Purpose of the Study:

  • To propose and evaluate a novel feature-engineering and machine learning-based approach for detecting DDoS attacks in SDNs.
  • To identify the most effective machine learning algorithm for DDoS attack detection within the SDN environment.
  • To provide a robust solution for enhancing SDN security against sophisticated cyber threats.

Main Methods:

  • Utilized the CSE-CIC-IDS2018 dataset, involving data cleaning, normalization, and feature selection using an improved binary grey wolf optimization algorithm.
  • Trained and evaluated multiple machine learning classifiers including Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbor (k-NN), Decision Tree, and XGBoost.
  • Selected the optimal classifier based on performance metrics and deployed it within the SDN controller for real-time detection.

Main Results:

  • The Random Forest (RF) algorithm demonstrated superior performance across various metrics such as accuracy, precision, recall, F1-score, and AUC values.
  • Comparative analysis confirmed the effectiveness of the proposed feature engineering and RF-based detection method over other evaluated algorithms.
  • The developed model successfully detected and identified DDoS attacks within the SDN environment.

Conclusions:

  • The proposed machine learning approach, particularly with the Random Forest classifier, offers an effective solution for detecting DDoS attacks in SDNs.
  • Feature engineering combined with optimized machine learning significantly enhances the ability to identify and mitigate cyber threats in software-defined networks.
  • This research provides a valuable contribution to the field of SDN security, offering a practical and high-performing detection strategy.