Phishing Websites Detection based on Meta heuristic Optimization and Ensemble Algorithms

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dc.contributor.authorAdewole, S. Kayode
dc.contributor.authorHambali, A. Moshood
dc.contributor.authorSaheed, Yakub
dc.contributor.authorAdeyemo, E. Victor
dc.contributor.authorJimoh, M. Kamaldeen
dc.contributor.authorIsiaka, M. Rafiu
dc.date.accessioned2026-05-16T19:53:41Z
dc.date.available2026-05-16T19:53:41Z
dc.date.issued2025-07-14
dc.description.abstractPhishing 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 analyzing 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 Optimization 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.
dc.identifier.urihttps://kwasuspace.kwasu.edu.ng/handle/123456789/7262
dc.publisherInternational Journal of Security and Networks
dc.titlePhishing Websites Detection based on Meta heuristic Optimization and Ensemble Algorithms
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