Title: Phishing websites detection based on meta-heuristic optimisation and ensemble algorithms

Authors: Kayode Sakariyah Adewole; Moshood Abiola Hambali; Yakub Kayode Saheed; Victor Elijah Adeyemo; Muhammed Kamaldeen Jimoh; Rafiu Mope Isiaka

Addresses: Department of Computer Science and Media Technology, Malmö University, Malmö, Sweden; Sustainable Digitalisation Research Centre, Malmö University, Malmö, Sweden ' Department of Computer Science, Federal University Wukari, Taraba, Nigeria ' School of IT and Computing, American University of Nigeria, Nigeria ' Department of Computer Science, Durham University, Durham, UK ' Department of Educational Technology, University of Ilorin, Ilorin, Nigeria ' Department of Computer Science, Kwara State University, Malete, Nigeria

Abstract: Phishing attacks pose a significant threat to web-and mobile-based applications, aiming to compromise network security and hijack legitimate resources. Illegitimate websites, fake emails, and deceptive web/mobile solutions are used to collect user credentials and compromise accounts. Traditional blacklist-based methods for detecting phishing websites suffer from a lack of zero-day attack detection. To address this, machine learning (ML) models have been widely explored. However, meta-heuristic approach that covers studies on feature selection to create compact ML models for phishing detection has not been extensively studied. This paper fills that gap by analysing ten meta-heuristic algorithms to identify discriminating features for detecting phishing websites. The study evaluates three ensemble-based classifiers - random forest, AdaBoost, and rotation forest. The results show that firefly optimisation algorithm (FOA) produced the best results. Using the features selected by FOA, random forest outperforms other ML algorithms with 95.36% accuracy and AUC-ROC of 98.9%. This result is followed by rotation forest (accuracy 94.89%) and AdaBoost performs the least with accuracy of 91.42%. The findings highlight the efficacy of the FOA meta-heuristic algorithm in phishing website detection.

Keywords: phishing; security; meta-heuristic; machine learning; ensemble algorithms; firefly optimisation algorithm; FOA.

DOI: 10.1504/IJSN.2026.153833

International Journal of Security and Networks, 2026 Vol.21 No.2, pp.65 - 85

Received: 16 Oct 2024
Accepted: 01 Jun 2025
Published online: 27 May 2026 *

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