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Deep Learning Based Real-time Phishing Email Detection

Year: 2026

Type: article

Abstract: Phishing emails remain a prominent cybersecurity threat, exploiting social engineering to compromise sensitive information. This study proposes a hybrid deep learning framework combining Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks for real-time phishing email detection. The model automatically extracts discriminative textual and structural features while capturing sequential dependencies within email content. Experimental evaluations on two scenarios—controlled and complex datasets— demonstrated the superior performance of the proposed model compared to traditional classifiers, achieving up to 98.5% accuracy with high precision, recall, and F1-score. Results indicate robust performance across diverse email structures, highlighting practical applicability for operational deployment. The study contributes to the advancement of automated email security systems in dynamic environments.

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.18457118

Authors: Saif Eldein Alfahal Suleiman Mohamed, Dr. Ibrahim Ahmed Mohammed

Language: English

Topic: Spam and Phishing Detection

Subfield: Information Systems

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W7127135200

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