Multiclass Imbalance Resampling Techniques for Network Intrusion Detection

Zawiyah Saharuna, Tohari Ahmad

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

Abstract

Security datasets often exhibit significant imbalances that can introduce bias during model training, diminish sensitivity to actual attacks, and lead to a substantial number of false negatives, potentially overlooking real threats. This is particularly evident in the highly skewed distribution of the UNSW-NB18 Bot-IoT dataset. To mitigate these issues, this study proposes implementing either Random Oversampling (ROS) or Synthetic Minority Oversampling (SMOTE) in conjunction with five ensemble algorithms to develop models for predicting intrusions in the Internet of Things networks. The results show that incorporating these methods with ensemble learners significantly improves model accuracy by 1 % to 4 % across the four algorithms compared to their absence. In addition, there were dramatic increases in precision, recall, and F1-score, achieving values between 95% and 100%.

Original languageEnglish
Title of host publication2024 10th International Conference on Smart Computing and Communication, ICSCC 2024
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages450-454
Number of pages5
ISBN (Electronic)9798350363104
DOIs
Publication statusPublished - 2024
Event10th International Conference on Smart Computing and Communication, ICSCC 2024 - Bali, Indonesia
Duration: 25 Jul 202427 Jul 2024

Publication series

Name2024 10th International Conference on Smart Computing and Communication, ICSCC 2024

Conference

Conference10th International Conference on Smart Computing and Communication, ICSCC 2024
Country/TerritoryIndonesia
CityBali
Period25/07/2427/07/24

Keywords

  • Imbalanced Data
  • IoT
  • Network Intrusion Detection
  • Network Security
  • Resampling

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