A Nested-Cascade Machine Learning Based Model for Intrusion Detection Systems

Romina Torres, Miguel A. Solis, Vicente Martinez, Rodrigo Salas

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

Abstract

In datasets, the preponderance of imbalanced classes impedes accurate cyberattack categorization. While high aggregate accuracy is sought, it's paramount to adeptly classify all attack types, especially the under-represented ones. Existing methodologies, such as Ensemble techniques and the Synthetic Minority Oversampling Technique (SMOTE), address these disparities, yet the dynamic nature of underrepresented cyberattacks in cybersecurity remains a concern. To address this, we introduce a nested cascade model tailored for diverse cyberattacks within imbalanced datasets. This model leverages binary classifiers across tiers, each targeting a specific attack type. Before initializing the cascade, SMOTE is applied to counterbalance class disparities. The cascade's classification sequence employs a dual strategy: an initial one-vs-all binary classifier approach for pending classes, followed by prioritization based on model performance. We assessed our approach using the UNSW-NB15 dataset. Preliminary results indicate approximately 80% efficiency across metrics like accuracy, recall, and Fl-score. Notably, SMOTE's in- tegration yielded significant improvements for underrepresented classes.

Original languageEnglish
Title of host publicationChileCon 2023 - 2023 IEEE CHILEAN Conference on Electrical, Electronics Engineering, Information and Communication Technologies
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9798350369533
DOIs
StatePublished - 2023
Externally publishedYes
Event2023 IEEE CHILEAN Conference on Electrical, Electronics Engineering, Information and Communication Technologies, ChileCon 2023 - Hybrid, Valdivia, Chile
Duration: 5 Dec 20237 Dec 2023

Publication series

NameProceedings - IEEE CHILEAN Conference on Electrical, Electronics Engineering, Information and Communication Technologies, ChileCon
ISSN (Print)2832-1529
ISSN (Electronic)2832-1537

Conference

Conference2023 IEEE CHILEAN Conference on Electrical, Electronics Engineering, Information and Communication Technologies, ChileCon 2023
Country/TerritoryChile
CityHybrid, Valdivia
Period5/12/237/12/23

Keywords

  • Intrusion detection
  • cascading
  • classification
  • cybersecurity
  • imbalanced dataset
  • machine learning

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