Title: Optimising intrusion detection systems using ensemble voting classifiers: a multi-dataset performance analysis

Authors: Sayantan Singha Roy; Amrita Bhadra; Munesh Chandra Trivedi; Awnish Kumar

Addresses: Department of Computer Science and Engineering, National Institute of Technology, Agartala, Tripura, India ' Department of Computer Science and Engineering, Adamas University, Kolkata, India ' Department of Computer Science and Engineering, National Institute of Technology, Agartala, Tripura, India ' Department of Computer Science and Engineering, National Institute of Technology, Agartala, Tripura, India

Abstract: As cyberattacks continue to increase in size and complexity, so does the need to protect sensitive data. Ensemble voting classifier has risen as one of the most extensive methods to improve the performance of intrusion detection systems which play a pivotal role in securing networks. The basis of this study is to use six common classifiers [logistic regression (LR), random forest (RF), decision tree (DT), AdaBoost (ADB), Gaussian Naive Bayes (GNB), and K-nearest neighbours (KNN)] on three benchmark datasets (KDD-CUP, CIC-DDoS2019, UNSW-NB15) and compare the classifiers given their final metrics (accuracy, recall, precision and F1-score). This study has explored 62 ensemble configurations to assess their suitability as base classifiers. Results show that ADB and RF ensembles perform best on KDD-CUP, ADB-DT-GNB-RF combinations excel on CIC-DDoS2019, and ADB-RF achieve top results on UNSW-NB15. The study provides practical guidance for developing data-driven IDS solutions and enhances ensemble-based cybersecurity research.

Keywords: ensemble machine learning; intrusion detection systems; IDSs; ensemble voting classifiers; benchmark datasets; comparative analysis.

DOI: 10.1504/IJAHUC.2026.155553

International Journal of Ad Hoc and Ubiquitous Computing, 2026 Vol.52 No.4, pp.193 - 211

Received: 27 Jun 2025
Accepted: 05 Jan 2026
Published online: 05 Aug 2026 *

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