Advancing Phishing Attack Detection with a Novel Dataset and Deep Learning Solution

Abstract

Phishing attacks, increasingly complex and accessible due to low cost and technical requirements, demand advanced detection methods. While recent machine learning-based approaches show promising results in preventing these threats, they still face limitations in terms of outdated training datasets and the number of extracted features. Therefore, in this paper, we introduce a novel phishing attack dataset with a high number of samples and dimensionality. We also propose a transformer-based deep learning model to detect phishing attacks accurately. Our experimental results on our dataset show a significant performance gain, achieving 98.13% accuracy, surpassing popular machine learning models and SAINT, a state-of-the-art deep learning model for tabular data.