Comparative Study of Content-Based Phishing Email Detection Using Global Vector (GloVe) and Bidirectional Encoder Representation from Transformer (BERT) Word Embedding Models

Abstract

Emails are the simplest way to communicate between users. But increasing rate of cybercrime activities require shrewd use of email. Phishing email is one of the most perilous threats nowadays. Through phishing email, attackers want to steal the sensitive information of the email users. This sensitive information includes username, password, credit card details etc. These may cause excessive financial loss. Many anti-phishing methods exist in literature to detect phishing emails. But criminals use new methods day by day, which causes challenge to build anti-phishing methods to prevent phishing emails. In this paper the performance of two deep learning models for detection of phishing email have been compared. The first model uses convolution neural network with Global Vector (GloVe) word embedding where Bidirectional Encoder Representations from Transformers (BERT) model with fine tuning has been used in second model. The proposed method detects the phishing email by analyzing the content of the email. Some widely used datasets (lingSpam, enronSpamSubset, completeSpamAssassin, Jose Nazario's phishing dataset and Enron email dataset) are merged together and applied to measure the performance of the model. It is found that the GloVe word embedding achieves better accuracy (98%) than BERT model (96%) in detection of phishing emails in the present context.