Phishing Classification Based on Text Content of an Email Body Using Transformers

Abstract

Phishing attacks steal sensitive credentials using different techniques, tools, and some sophisticated methods. The techniques include content injection, information re-routing, social engineering, server hacking, social networking, SMS and WhatsApp mobile applications. To overcome such attacks and minimize risks of such attacks, many phishing detection and avoidance techniques were introduced. Among various techniques, deep learning algorithms achieved the efficient results. In the proposed work, a transformers-based technique is used to classify phishing emails. The proposed method outperformed the other similar mechanisms for the classification of phishing emails. The phishing classification accuracy achieved by the proposed work is 99.51% using open-source datasets. The proposed model is also used to learn and validate the correctness of the in-house created datasets. The obtained results with in-house datasets are equally competitive. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.