journal article·2024Open access

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

Arar Al Tawil, Laiali Almazaydeh, Doaa Qawasmeh, Baraah Qawasmeh, Mohammad Alshinwan, Khaled Elleithy

Computers, Materials & Continua· Volume 81· Issue 2

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 Fr... | Find, read and cite all the research you need on Tech Science Press
Source: openalex_inverted_index · Quality: baseline

Research context

OpenAlex topics: Spam and Phishing Detection

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

References (38)

Show source-provided references
1. An overview of Internet of Things10.12928/telkomnika.v18i5.15911 ↗
2. An overview of Internet of Things (IoT): Architectural aspects, challenges, and protocols10.1002/cpe.4946 ↗
3. A comprehensive review study of cyber-attacks and cyber security; Emerging trends and recent developments10.1016/j.egyr.2021.08.126 ↗
4. Cyber terrorism cases and stock market valuation effects10.1108/ICS-09-2022-0147 ↗
5. Deep cybersecurity: A comprehensive overview from neural network and deep learning perspective10.1007/s42979-021-00535-6 ↗
6. Cybersecurity for industrial control systems: A survey10.1016/j.cose.2019.101677 ↗
7. Simulation for cybersecurity: State of the art and future directions10.1093/cybsec/tyab005 ↗
8. Machine learning in cybersecurity: A comprehensive survey
9. Performance comparison and current challenges of using machine learning techniques in cybersecurity10.3390/en13102509 ↗
10. Machine learning for cybersecurity in smart grids: A comprehensive review-based study on methods, solutions, and prospects10.1016/j.ijcip.2022.100547 ↗
11. A systematic review of defensive and offensive cybersecurity with machine learning10.3390/app10175811 ↗
12. URL phishing detection using machine learning techniques based on URLs lexical analysis
13. A comparison of machine learning techniques for phishing detection
14. Analyzing and predicting spear-phishing using machine learning methods10.35925/j.multi.2020.4.30 ↗
15. Detection of phishing emails using machine learning, deep learning
16. Machine learning based phishing E-mail detection
17. A machine learning approach towards phishing email detection CEN-Security@IWSPA 2018
18. Phishing detection in E-mails using machine learning
19. A phishing-attack-detection model using natural language processing and deep learning10.3390/app13095275 ↗
20. Phishing_Mail, “Kaggle,” Accessed: Jul. 13, 2023. [Online]. Available: https://www.kaggle.com/datasets/somumourya/fishing-mail
21. Phishing detection using random forest, SVM and neural network with backpropagation
22. Identification of phishing attack in websites using random forest-SVM hybrid model
23. Applying machine learning algorithms for detecting phishing websites: Applications of SVM, KNN, decision trees, and random forests
24. PDMLP: Phishing detection using multilayer perceptron
25. Efficient prediction of phishing websites using multilayer perceptron (MLP)
26. Performance evaluation of logistic regression and neural network for credit risk assessment
27. J. Devlin, M. W. Chang, K. Lee, and K. Toutanova, “BERT: Pre-training of deep bidirectional transformers for language understanding,” 2019, arXiv:1810.04805.
28. Y. Liu et al., “RoBERTa: A robustly optimized BERT pretraining approach,” 2019, arXiv:1907.11692.
29. V. Sanh, L. Debut, J. Chaumond, and T. Wolf, “DistilBERT, a distilled version of BERT: Smaller, faster, cheaper and lighter,” 2019, arXiv:1910.01108.
30. Z. Lan, M. Chen, S. Goodman, K. Gimpel, P. Sharma and R. Soricut, “ALBERT: A lite BERT for self-supervised learning of language representations,” 2020, arXiv:1909.11942.
31. A review on evaluation metrics for data classification evaluations10.5121/ijdkp.2015.5201 ↗
32. Clinical text classification with word representation features and machine learning algorithms10.3991/ijoe.v19i04.36099 ↗
33. A. Nayak, H. Timmapathini, K. Ponnalagu, and V. Gopalan Venkoparao, “Domain adaptation challenges of BERT in tokenization and sub-word representations of out-of-vocabulary words,” presented at the 1st Workshop Insights Negat. Results NLP, Nov. 2020, pp. 1–5.10.18653/v1/2020.insights-1.1 ↗
34. Prompt engineering or fine-tuning? A case study on phishing detection with large language models10.3390/make6010018 ↗
35. A comprehensive survey on word representation models: From classical to state-of-the-art word representation language models10.1145/3434237 ↗
36. Jones · A Statistical Interpretation of Term Specificity and Its Application in Retrieval · 1988 · 123
37. Efficient estimation of word representations in vector space
38. Short text classification based on Wikipedia and Word2vec