Implementation of ensemble learning and feature selection for performance improvements in anomaly-based intrusion detection systems

Qusyairi Ridho Saeful Fitni, Kalamullah Ramli

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

4 Citations (Scopus)

Abstract

In recent years, data security in organizational information systems has become a serious concern. Many attacks are becoming less detectable by firewall and antivirus software. To improve security, intrusion detection systems (IDSs) are used to detect anomalies in network traffic. Currently, IDS technology has performance issues regarding detection accuracy, detection times, false alarm notifications, and unknown attack detection. Several studies have applied machine-learning approaches as solutions. This study used an ensemble learning approach that integrates the benefits of each single detection algorithms. We made comparisons with seven single classifiers to identify the most appropriate basic classifiers for ensemble learning. The experiment shows logistics regression, decision trees, and gradient boosting are chosen for our ensemble model. The Communications Security Establishment and Canadian Institute for Cybersecurity 2018 (CSE-CIC-IDS2018) dataset was used to evaluate the proposed model. Spearman's rank correlation coefficient facilitated the identification of the data features that might not be used. The experiment results showed that 23 of the 80 features were selected, and the model achieved the following scores: final accuracy, 98.8%; precision, 98.8%; recall, 97.1%; and F1, 97.9%.

Original languageEnglish
Title of host publicationProceedings - 2020 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology, IAICT 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages118-124
Number of pages7
ISBN (Electronic)9781728193366
DOIs
Publication statusPublished - Jul 2020
Event2020 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology, IAICT 2020 - Bali, Indonesia
Duration: 7 Jul 20208 Jul 2020

Publication series

NameProceedings - 2020 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology, IAICT 2020

Conference

Conference2020 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology, IAICT 2020
Country/TerritoryIndonesia
CityBali
Period7/07/208/07/20

Keywords

  • CSE-CIC-IDS2018
  • ensemble learning method
  • features selection
  • intrusion detection

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