Feature Selection Using Pearson Correlation with Lasso Regression for Intrusion Detection System

Iwan Handovo Putro, Tohari Ahmad

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

Abstract

The growth of Internet users and traffic drives significant changes in the network security domain. Computer networks become increasingly vulnerable to attack by irresponsible parties, which can potentially cause substantial loss and damage due to information stealing, sending malicious packets, and overtaking network resources. While the effort to secure the network has been conducted persistently, unfortunately, the variety and volume of cyber threats have continuously increased. As such, there is a demand for an effective and efficient attack detection model to prevent this catastrophic network failure by implementing feature selection techniques to reduce the dimension of the feature on a dataset. We proposed a feature selection method combining the Pearson Correlation method with Lasso Regression to address that need. The Pearson Correlation is used to select the best feature based on its degree of relationship. After that, the selected result is optimized using Lasso Regression to achieve the best features for the IDS model. This study is conducted using the UNSWNB-15 dataset and it is revealed that this proposed method significantly improves the SVM classifier's accuracy and false positive rate from 76.45% to 97.17% and 20.67% to 1.44%, respectively.

Original languageEnglish
Title of host publication12th International Symposium on Digital Forensics and Security, ISDFS 2024
EditorsAsaf Varol, Murat Karabatak, Cihan Varol, Eva Tuba
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9798350330366
DOIs
Publication statusPublished - 2024
Externally publishedYes
Event12th International Symposium on Digital Forensics and Security, ISDFS 2024 - San Antonio, United States
Duration: 29 Apr 202430 Apr 2024

Publication series

Name12th International Symposium on Digital Forensics and Security, ISDFS 2024

Conference

Conference12th International Symposium on Digital Forensics and Security, ISDFS 2024
Country/TerritoryUnited States
CitySan Antonio
Period29/04/2430/04/24

Keywords

  • Feature Selection
  • Intrusion Detection System
  • Lasso Regression
  • Pearson Correlation

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