Andre Sebastian Samaniego Buñay

Comparative Analysis of Transformer-Based and ClassicalMachine Learning Models for Phishing Email Detection:A Multi-Source Dataset Evaluation with Explainability

This paper compares five classifiers: Multinomial Naive Bayes, Random Forest, Bidirectional Long Short-Term Memory, BiLSTM, DistilBERT, and BERT-base, and finds that BERT-base achieves the highest F1-score and DistilBERT the lowest, representing the strongest accuracy–latency trade-off in the evaluated environment.