A feature probability estimation-based feature selection approach for intrusion detection

Mahendra Prasad, Sachin Tripathi, Keshav Dahal

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

This work proposes a probabilistic feature estimation-based feature selection method for intrusion detection systems. The method aims to reduce the number of insignificant features from the training set, which helps decrease the processing complexity and storage requirements during model training. It efficiently removes redundant or insignificant features and makes the system faster and more reliable. The proposed method applies to datasets that contain both data types: numerical and categorical data. We have compared the method with several well-known feature selection techniques: Principal Component Analysis (PCA), K-best (Chi-Square) feature selection method, Feature Importance method, and Recursive Feature Elimination (RFE) method. The experimental results show that the proposed method also shows a similar performance to existing techniques. The proposed method easily deals with large and complex datasets.
Original languageEnglish
Title of host publicationProceedings of the 6th International Conference on Recent Advances in Information Technology (RAIT 2025)
Number of pages6
Publication statusAccepted/In press - 15 Dec 2024
Event6th International Conference on Recent Advances in Information Technology - Indian Institute of Technology, Dhanbad, India
Duration: 6 Mar 20258 Mar 2025
https://people.iitism.ac.in/~rait/

Conference

Conference6th International Conference on Recent Advances in Information Technology
Abbreviated titleRAIT 2025
Country/TerritoryIndia
CityDhanbad
Period6/03/258/03/25
Internet address

Keywords

  • feature selection
  • intrusion detection system
  • machine learning
  • feature probability estimation (FPE)

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