Feature Selection Ranking and Subset-Based Techniques with Different Classifiers for Intrusion Detection

  • Rania A. GhazyEmail author
  • El-Sayed M. El-Rabaie
  • Moawad I. Dessouky
  • Nawal A. El-Fishawy
  • Fathi E. Abd El-Samie


This paper investigates the performance of different feature selection techniques such as ranking and subset-based techniques, aiming to find the optimum collection of features to detect attacks with an appropriate classifier. The results reveal that more accuracy of detection and less false alarms are obtained after eliminating the redundant features and determining the most useful set of features, which increases the intrusion detection system (IDS) performance.


Feature selection Intrusion detection Classifiers Network attacks 



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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Rania A. Ghazy
    • 1
    Email author
  • El-Sayed M. El-Rabaie
    • 2
  • Moawad I. Dessouky
    • 2
  • Nawal A. El-Fishawy
    • 3
  • Fathi E. Abd El-Samie
    • 2
  1. 1.University of Sadat CityEl-Sadat CityEgypt
  2. 2.Department of Electronics and Electrical Communications Engineering, Faculty of Electronic EngineeringMenoufia UniversityMenoufEgypt
  3. 3.Department of Computer Science and Engineering, Faculty of Electronic EngineeringMenoufia UniversityMenoufEgypt

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