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An improved equilibrium optimization algorithm for feature selection problem in network intrusion detection.

Zahra Asghari Varzaneh1, Soodeh Hosseini2

  • 1Department of Computer Science, Faculty of Mathematics and Computer, Shahid Bahonar University of Kerman, Kerman, Iran.

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|August 12, 2024
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Summary
This summary is machine-generated.

A new algorithm, Levy-opposition-equilibrium optimization (LOEO), enhances network intrusion detection systems (IDSs) by effectively selecting crucial features. This method improves detection accuracy while significantly reducing data dimensionality.

Keywords:
Equilibrium optimizerFeature selectionIntrusion detection systemLevy flightOpposition-based learning

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Area of Science:

  • Cybersecurity
  • Artificial Intelligence
  • Machine Learning

Background:

  • Network Intrusion Detection Systems (IDSs) face challenges with high-dimensional feature spaces, containing irrelevant or redundant data.
  • Effective feature selection is critical for improving the performance and efficiency of IDSs.

Purpose of the Study:

  • To propose an enhanced Equilibrium Optimization (EO) algorithm, named Levy-opposition-equilibrium optimization (LOEO), for effective feature selection in IDSs.
  • To develop a binary version, BLOEO, to intelligently identify the most informative feature subsets.

Main Methods:

  • The proposed LOEO algorithm integrates opposition-based learning (OBL) to enhance population diversity.
  • Levy flight is utilized within LOEO to help the algorithm escape local optima.
  • The binary rendition, BLOEO, is specifically applied for feature selection in IDSs.

Main Results:

  • Empirical evaluations on NSL-KDD, UNSW-NB15, and CIC-IDS2017 datasets validate the effectiveness of the BLOEO algorithm.
  • BLOEO demonstrates a strong ability to reduce the number of features while maintaining high intrusion detection accuracy (over 95%).
  • On the UNSW-NB15 dataset, BLOEO achieved 97.6% accuracy and 100% precision using only an average of 10.8 features.

Conclusions:

  • The BLOEO algorithm offers a robust solution for feature selection in network intrusion detection.
  • It significantly enhances IDS performance by reducing feature space complexity and improving detection accuracy.