Cyber Threats and Network Intrusion Detection: A Comparative Analysis of Soft and Hard Voting Ensemble Methods

Authors

• Muath AlShaikh, Cyber Security Department, College of Engineering, Al Ain University, Abu Dhabi, UAEFollow
• Farid Binbeshr, Interdisciplinary Research Center for Intelligent Secure Systems (IRC-ISS), King Fahd University of Petroleum and Minerals (KFUPM), Dhahran, 31261, Saudi ArabiaFollow
• Jumana Alamoudi, College of Computers and Information Technology, Hadhramout University, AL-Mukalla, 50512, Hadhramout, YemenFollow
• Sheikh Bawazer, College of Computers and Information Technology, Hadhramout University, AL-Mukalla, 50512, Hadhramout, YemenFollow
• Ala Hag, Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Victoria, AustraliaFollow

Corresponding Author

Muath AlShaikh

Authors ORCID

Muath AlShaikh: https://orcid.org/0000-0001-5550-7659

Farid Binbeshr: https://orcid.org/0000-0001-8630-1784

Document Type

Article

Keywords

Cybersecurity, Network traffic, Machine learning IDS, Ensemble learning

Abstract

More reliable techniques for detecting network intrusions are needed due to the growing supremacy of cyber attacks. Despite using signature-based techniques, traditional intrusion detection systems (IDS) are still not very successful against new threats. A thorough comparison of soft and hard voting ensemble techniques for binary network traffic classification is provided in this article. To create a powerful classifier system, the suggested approach combines five distinct supervised machine learning (ML) techniques. This study uses the UNSW-NB15 dataset, and correlation analysis is used extensively in feature selection to determine the optimal intrusion detection settings. According to experimental results, group approaches reach 99.73% accuracy utilizing the soft vote technique and surpass standalone classifiers. Excellent results from the classifier system include 99.22% precision, 99.71% recall. The classifier system has excellent performances such as 99.22% precision, 99.71% recall, and 99.46% F1 score, which indicates an extremely low false positive rate of 0.00421.

How to Cite This Article

AlShaikh, Muath; Binbeshr, Farid; Alamoudi, Jumana; Bawazer, Sheikh; and Hag, Ala (2026) "Cyber Threats and Network Intrusion Detection: A Comparative Analysis of Soft and Hard Voting Ensemble Methods," Mesopotamian Journal of CyberSecurity: Vol. 6: Iss. 1, Article 2.
DOI: https://doi.org/10.68212/2958-6542.1131