TY  - EJOU
AU  - Gupta, Brij B. 
AU  - Gaurav, Akshat 
AU  - Arya, Varsha 
AU  - Attar, Razaz Waheeb 
AU  - Bansal, Shavi 
AU  - Alhomoud, Ahmed 
AU  - Chui, Kwok Tai 

TI  - Advanced BERT and CNN-Based Computational Model for Phishing Detection in Enterprise Systems
T2  - Computer Modeling in Engineering \& Sciences

PY  - 2024
VL  - 141
IS  - 3
SN  - 1526-1506

AB  - Phishing attacks present a serious threat to enterprise systems, requiring advanced detection techniques to protect sensitive data. This study introduces a phishing email detection framework that combines Bidirectional Encoder Representations from Transformers (BERT) for feature extraction and CNN for classification, specifically designed for enterprise information systems. BERT’s linguistic capabilities are used to extract key features from email content, which are then processed by a convolutional neural network (CNN) model optimized for phishing detection. Achieving an accuracy of 97.5%, our proposed model demonstrates strong proficiency in identifying phishing emails. This approach represents a significant advancement in applying deep learning to cybersecurity, setting a new benchmark for email security by effectively addressing the increasing complexity of phishing attacks.
KW  - Phishing; BERT; convolutional neural networks; email security; deep learning

DO  - 10.32604/cmes.2024.056473