What question did this study set out to answer?

The aim is to enhance the detection of phishing URLs using a deep learning model that combines text and structural features.

March 26, 2026Open Access

Phishing Website Detection using BERT & Handcrafted features

Key Points

The aim is to enhance the detection of phishing URLs using a deep learning model that combines text and structural features.
Implemented a fine-tuned BERT model for textual analysis of URLs.
Incorporated handcrafted structural features like URL length and special characters.
Evaluated model performance on a dataset of phishing and legitimate URLs.
Demonstrated effective classification between phishing and legitimate URLs.
Showed improved generalization and detection accuracy compared to traditional methods.

Abstract

This paper presents a phishing URL detection system using a fine-tuned BERT-based multimodal approach. The model combines textual analysis of URLs with structural features such as URL length, number of digits, special characters, and HTTPS presence. The objective is to improve generalization and detection accuracy for phishing attacks. The system is evaluated on a sample dataset and demonstrates effective classification between phishing and legitimate URLs. This work highlights the application of deep learning in enhancing cybersecurity solutions.

Phishing Website Detection using BERT & Handcrafted features

Key Points

Abstract

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