One of the most harmful and deceptive forms of cybercrime is phishing, which targets users with malicious emails and websites. In this paper, we focus on the use of natural language processing (NLP) techniques and transformer models for phishing email detection. The Nazario Phishing Corpus is preprocessed and blended with real emails from the Enron dataset to create a robustly balanced dataset. Urgency, deceptive phrasing, and structural anomalies were some of the neglected features and sociolinguistic traits of the text, which underwent tokenization, lemmatization, and noise filtration. We fine-tuned two transformer models, Bidirectional Encoder Representations from Transformers (BERT) and the Robustly Optimized BERT Pretraining Approach (RoBERTa), for binary classification. The models were evaluated on the standard metrics of accuracy, precision, recall, and F1-score. Given the context of phishing, emphasis was placed on recall to reduce the number of phishing attacks that went unnoticed. The results show that RoBERTa has more general performance and fewer false negatives than BERT and is therefore a better candidate for deployment on security-critical tasks.
Ibrahim et al. (Sat,) studied this question.
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