What question did this study set out to answer?

The aim is to enhance phishing email detection through a novel hybrid deep learning model combining BERT and BLSTM.

April 4, 2026Open Access

A hybrid stacked bidirectional long short-term memory and Bidirectional Encoder Representations from Transformers model for enhanced phishing email detection

Key Points

The aim is to enhance phishing email detection through a novel hybrid deep learning model combining BERT and BLSTM.
Developed a hybrid deep learning model integrating BERT and BLSTM.
Utilized a phishing email dataset for evaluation.
Measured performance using F1-score, recall, and precision.
The hybrid model significantly outperformed baseline models in key metrics.
Improved detection rates seen in F1-score, recall, and precision.
Demonstrated benefits of combining transformer-based and recurrent architectures.

Abstract

Phishing attacks have been among the latest cybersecurity threats as they exploit people’s weaknesses and compromise critical information. Traditional methodologies of phishing detection practices based on machine learning have generally been unsuccessful in offering high performance due to limited generalizability and very high false positive rates. In this paper, a novel hybrid deep learning model is proposed that incorporates Bidirectional Encoding Representation from Transformers (BERT) with Bidirectional Long Short-TermMemory (BLSTM) to increase the detection of suspicious emails within the framework of the proposed model. The model utilizes the contextual understanding of texts from BERT and sequential learning from BLSTM to enhance classification accuracy. Experimental results using an available phishing email dataset show that the proposed approach significantly outperforms the baseline models in F1-score, recall, and precision metrics. Those results highlight the advantages of combining transformerbased embedding and recurrent architectures in phishing detection, providing avenues towards a more robust framework for email security enhancement.

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