Authors : Zainab Jibril Amedu; Prema Kirubakaran; Dr. Ridwan Kolapo

Volume/Issue : Volume 11 - 2026, Issue 5 - May

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Google Scholar : https://tinyurl.com/5ct56ubj

Scribd : https://tinyurl.com/yc6rs48t

DOI : https://doi.org/10.38124/ijisrt/26May1421

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

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Abstract : This paper presents a novel hybrid deep learning architecture for phishing detection that integrates BERT and Graph Neural Networks through cross-modal attention fusion. The proposed model addresses the multimodal nature of phishing attacks by simultaneously processing textual features via DistilBERT and structural relationships via a Heterogeneous Graph Transformer. Our methodology employs a security-aware loss function emphasizing false positive reduction and implements 5-fold cross-validation for robust evaluation.

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