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Towards an Optimized Ensemble Feature Selection for DDoS Detection Using Both Supervised and Unsupervised Method.

Sajal Saha1, Annita Tahsin Priyoti1, Aakriti Sharma1

  • 1Department of Computer Science, Western University, London, ON N6A 3K7, Canada.

Sensors (Basel, Switzerland)
|December 11, 2022
PubMed
Summary
This summary is machine-generated.

An ensemble feature selection (EnFS) method provides a universal best feature set for artificial intelligence (AI) models, outperforming individual methods in detecting cyber-attacks like Distributed Denial-of-Service (DDoS). This approach enhances AI model generalization and learning speed for better security.

Keywords:
DDoSdeep learningensemblemachine learningunsupervised model

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Area of Science:

  • Cybersecurity
  • Artificial Intelligence
  • Machine Learning

Background:

  • Increasing demand for digital services drives cyber-attacks like Distributed Denial-of-Service (DDoS).
  • AI-based detection systems require high-quality data and optimal feature subsets for accuracy.
  • Existing research lacks a common optimal feature set applicable across machine learning, deep learning, and unsupervised learning models.

Purpose of the Study:

  • To investigate and evaluate 15 individual feature selection (FS) methods.
  • To assess the performance of filter-based, wrapper-based, embedded, and ensemble feature selection (EnFS) techniques.
  • To identify a universal best feature set for diverse AI models in DDoS detection.

Main Methods:

  • Extensive evaluation of 15 individual FS methods.
  • Application of supervised and unsupervised learning for feature subset quality assessment.
  • Comparison of individual FS methods against an ensemble feature selection (EnFS) technique.

Main Results:

  • The ensemble feature selection (EnFS) method demonstrated superior performance compared to individual FS methods.
  • A common, high-performing feature subset was identified.
  • The EnFS method provides a universal best feature set suitable for all AI model types.

Conclusions:

  • Ensemble feature selection is crucial for optimizing AI models in cybersecurity.
  • The identified universal feature set enhances the generalization and learning speed of AI-based DDoS detection systems.
  • This study establishes a benchmark for feature selection in AI-driven cyber defense.