A Robust Ensemble Learning Approach to URL-Based Phishing Webpage Detection
Year: 2026
Type: article
Abstract: The proliferation of online fraud has resulted in substantial financial damage to individuals and organizations alike, with web phishing emerging as one of the most pervasive and harmful attack vectors. In response, this paper proposes the Stacking Ensemble Models Generator (SEMG), a URL-based phishing detection approach that leverages a multi-objective Genetic Algorithm to jointly optimize Precision and Recall in the selection and configuration of stacking ensemble models. An initial pool of base learners is trained on labeled datasets and subsequently evolved through genetic operators toward a globally optimal ensemble. Experimental evaluation across five datasets sourced from Mendeley and UCI repositories demonstrates that SEMG consistently surpasses individual base learners and compares favorably against existing methods, attaining 99.2% performance across all metrics on D2 while matching or exceeding state-of-the-art results on the remaining benchmarks. These outcomes underscore the framework’s robustness and its potential for deployment in real-world phishing detection systems.
Source: Big Data and Cognitive Computing
DOI: 10.3390/bdcc10050136
Authors: Abdellah Rezoug, Mohamed Bader-el-den
Institutions: University of Boumerdes, University of Portsmouth
Language: English
Topic: Spam and Phishing Detection
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,172
OpenAlex ID: W7155941360