Open Access

ARTICLE

Comparative Analysis of Machine Learning Algorithms for Email Phishing Detection Using TF-IDF, Word2Vec, and BERT

by Arar Al Tawil^1,*, Laiali Almazaydeh², Doaa Qawasmeh³, Baraah Qawasmeh⁴, Mohammad Alshinwan^1,5, Khaled Elleithy⁶

1 Faculty of Information Technology, Applied Science Private University, Amman, 11931, Jordan
2 College of Engineering, Abu Dhabi University, Abu Dhabi, Al Ain, P.O. Box 1790, United Arab Emirates
3 Faculty of Artificial Intelligence, Al-Balqa Applied University, Salt, 19117, Jordan
4 Department of Civil and Construction Engineering, Western Michigan University, Kalamazoo, MI 49008, USA
5 MEU Research Unit, Middle East University, Amman, 11831, Jordan
6 Department of Computer Science and Engineering, University of Bridgeport, Bridgeport, CT 06604, USA

* Corresponding Author: Arar Al Tawil. Email:

Computers, Materials & Continua 2024, 81(2), 3395-3412. https://doi.org/10.32604/cmc.2024.057279

Received 14 August 2024; Accepted 15 October 2024; Issue published 18 November 2024

Abstract

Cybercriminals often use fraudulent emails and fictitious email accounts to deceive individuals into disclosing confidential information, a practice known as phishing. This study utilizes three distinct methodologies, Term Frequency-Inverse Document Frequency, Word2Vec, and Bidirectional Encoder Representations from Transformers, to evaluate the effectiveness of various machine learning algorithms in detecting phishing attacks. The study uses feature extraction methods to assess the performance of Logistic Regression, Decision Tree, Random Forest, and Multilayer Perceptron algorithms. The best results for each classifier using Term Frequency-Inverse Document Frequency were Multilayer Perceptron (Precision: 0.98, Recall: 0.98, F1-score: 0.98, Accuracy: 0.98). Word2Vec’s best results were Multilayer Perceptron (Precision: 0.98, Recall: 0.98, F1-score: 0.98, Accuracy: 0.98). The highest performance was achieved using the Bidirectional Encoder Representations from the Transformers model, with Precision, Recall, F1-score, and Accuracy all reaching 0.99. This study highlights how advanced pre-trained models, such as Bidirectional Encoder Representations from Transformers, can significantly enhance the accuracy and reliability of fraud detection systems.

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Keywords

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