Phishing could be considered a worldwide problem; undoubtedly, the number of illegal websites has increased quickly. Besides that, phishing is a security attack that has several purposes, such as personal information, credit card numbers, and other information. Phishing websites look like legitimate ones, which makes it difficult to differentiate between them. There are several techniques and methods for phishing detection. The authors present two machine-learning algorithms for phishing detection. Besides that, the algorithms employed are XGBoost and random forest. Also, this study uses particle swarm optimization (PSO) and grey wolf optimizer (GWO), which are considered metaheuristic algorithms. This research used the Mendeley dataset. Precision, recall, and accuracy are used as the evaluation criteria. Experiments are done with all features (111) and with features selected by PSO and GWO. Finally, experiments are done with the most common features selected by both PSO and GWO (PSO ∩ GWO). The result demonstrates that system performance is highly acceptable, with an F-measure of 91.4%.
Collaboration network of the authors
10 authors ·
25 co-authorship links
· 18 across different organisations
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World Islamic Sciences and Education University
Teesside University
American University in the Emirates
Higher Colleges of Technology
King's College London
Different organisations
Same organisation
Affiliation unknown
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