Comparative Investigation of Traditional Machine-Learning Models and Transformer Models for Phishing Email Detection

Abstract

Phishing emails pose a significant threat to cybersecurity worldwide. There are already tools that mitigate the impact of these emails by filtering them, but these tools are only as reliable as their ability to detect new formats and techniques for creating phishing emails. In this paper, we investigated how traditional models and transformer models work on the classification task of identifying if an email is phishing or not. We realized that transformer models, in particular distilBERT, BERT, and roBERTa, had a significantly higher performance compared to traditional models like Logistic Regression, Random Forest, Support Vector Machine, and Naive Bayes. The process consisted of using a large and robust dataset of emails and applying preprocessing and optimization techniques to maximize the best result possible. roBERTa showed an outstanding capacity to identify phishing emails by achieving a maximum accuracy of 0.9943. Even though they were still successful, traditional models performed marginally worse; SVM performed the best, with an accuracy of 0.9876. The results emphasize the value of sophisticated text-processing methods and the potential of transformer models to improve email security by thwarting phishing attempts.