PHISHING AND FRAUD DETECTION IN IMBALANCED DATASETS: A COMPARATIVE STUDY OF ENSEMBLE AND DEEP LEARNING MODELS

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dc.contributor.authorMaria Khalil
dc.contributor.authorNael Abu Halaweh
dc.date.accessioned2026-09-15T06:39:50Z
dc.date.available2026-09-15T06:39:50Z
dc.date.issued2026-05-12
dc.description.abstractPhishing and fraud attacks remain major challenges in cybersecurity, particularly due to class imbalance, where malicious cases are significantly fewer than legitimate ones. This imbalance complicates detection and often reduces model performance. Although deep learning is widely regarded as a powerful approach, it does not always perform effectively on tabular and highly imbalanced data, highlighting the need to evaluate alternative methods.
dc.identifier.urihttps://dspace.alquds.edu/handle/20.500.12213/10824
dc.language.isoen
dc.publisherDeanship of Scientific Research - Al-Quds University
dc.titlePHISHING AND FRAUD DETECTION IN IMBALANCED DATASETS: A COMPARATIVE STUDY OF ENSEMBLE AND DEEP LEARNING MODELS
dc.typeArticle
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