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Original Article
AI-Driven Phishing Detection Using Natural Language Processing and Machine Learning
Sarveena S1
Dhanusiya R2
A. Raja3
1 2 Department of Computer Science and Engineering (Cyber Security), United Institute of Technology, Coimbatore, Tamil Nadu, India. 3 Head of the Department, Department of Computer Science and Engineering (Cyber Security), United Institute of Technology, Coimbatore, Tamil Nadu, India.
Published Online: May-June 2026
Pages: 120-128
Cite this article
↗ https://www.doi.org/10.59256/ijire.20260703013
References
1. A. K. Jain and B. B. Gupta, "Phishing detection: Analysis of visual similarity-based approaches," Security and Communication
Networks, vol. 2017, Article ID 5421046, 2017.
2. S. Marchal, J. Francois, R. State, and T. Engel, "PhishStorm: Detecting phishing with streaming analytics," IEEE Transactions on
Network and Service Management, vol. 11, no. 4, pp. 458-471, 2014.
3. R. Verma and N. Hossain, "Semantic feature selection for text with application to phishing email detection," in Proceedings of the
IEEE ICC, 2017.
4. J. Ma, L. K. Saul, S. Savage, and G. M. Voelker, "Learning to detect malicious URLs," ACM Transactions on Intelligent Systems, vol.
2, no. 3, Art. 30, 2011.
5. M. Aburrous, M. A. Hossain, K. Dahal, and F. Thabtah, "Intelligent phishing detection system for e-banking using fuzzy data mining,"
Expert Systems with Applications, vol. 37, no. 12, pp. 7913-7921, 2010.
6. I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning. Cambridge, MA: MIT Press, 2016.7. T. Mikolov, I. Sutskever, K. Chen, G. Corrado, and J. Dean, "Distributed representations of words and phrases and their
compositionality," in Advances in Neural Information Processing Systems (NeurIPS), 2013, pp. 3111-3119.
8. J. Pennington, R. Socher, and C. D. Manning, "GloVe: Global vectors for word representation," in Proc. EMNLP, Doha, Qatar, 2014,
pp. 1532-1543.
9. Y. Kim, "Convolutional neural networks for sentence classification," in Proc. EMNLP, Doha, Qatar, 2014, pp. 1746-1751.
10. A. Vaswani et al., "Attention is all you need," in Advances in Neural Information Processing Systems (NeurIPS), 2017, pp. 5998-6008.
11. J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, "BERT: Pre-training of deep bidirectional transformers for language
understanding," in Proc. NAACL-HLT, 2019, pp. 4171-4186.
12. F. Pedregosa et al., "Scikit-learn: Machine learning in Python," Journal of Machine Learning Research, vol. 12, pp. 2825-2830, 2011.
13. D. Jurafsky and J. H. Martin, Speech and Language Processing, 3rd ed. Upper Saddle River, NJ: Pearson, 2020.
14. S. Hochreiter and J. Schmidhuber, "Long short-term memory," Neural Computation, vol. 9, no. 8, pp. 1735-1780, 1997.
15. Anti-Phishing Working Group (APWG), "Phishing Activity Trends Report, Q4 2023," APWG, Tech. Rep., 2024.
16. R. Basnet, S. Mukkamala, and A. H. Sung, "Detection of phishing attacks: A machine learning approach," in Soft Computing
Applications in Industry, vol. 226, 2008, pp. 373-383.
17. T. Moore and R. Clayton, "Examining the impact of website take-down on phishing," in Proc. APWG eCrime Researchers Summit,
2007.
18. G. Salton and M. J. McGill, Introduction to Modern Information Retrieval. New York: McGraw-Hill, 1983.
19. Z. Zhang, J. Ma, J. Han, and X. Niu, "Email phishing detection by analysing natural language characteristics," in Proc. IEEE CCECE,
2007.
20. K. L. Chiew, K. S. C. Yong, and C. L. Tan, "A survey of phishing attacks: Their types, vectors and technical approaches," Expert
Systems with Applications, vol. 106, pp. 1-20, 2018.
Networks, vol. 2017, Article ID 5421046, 2017.
2. S. Marchal, J. Francois, R. State, and T. Engel, "PhishStorm: Detecting phishing with streaming analytics," IEEE Transactions on
Network and Service Management, vol. 11, no. 4, pp. 458-471, 2014.
3. R. Verma and N. Hossain, "Semantic feature selection for text with application to phishing email detection," in Proceedings of the
IEEE ICC, 2017.
4. J. Ma, L. K. Saul, S. Savage, and G. M. Voelker, "Learning to detect malicious URLs," ACM Transactions on Intelligent Systems, vol.
2, no. 3, Art. 30, 2011.
5. M. Aburrous, M. A. Hossain, K. Dahal, and F. Thabtah, "Intelligent phishing detection system for e-banking using fuzzy data mining,"
Expert Systems with Applications, vol. 37, no. 12, pp. 7913-7921, 2010.
6. I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning. Cambridge, MA: MIT Press, 2016.7. T. Mikolov, I. Sutskever, K. Chen, G. Corrado, and J. Dean, "Distributed representations of words and phrases and their
compositionality," in Advances in Neural Information Processing Systems (NeurIPS), 2013, pp. 3111-3119.
8. J. Pennington, R. Socher, and C. D. Manning, "GloVe: Global vectors for word representation," in Proc. EMNLP, Doha, Qatar, 2014,
pp. 1532-1543.
9. Y. Kim, "Convolutional neural networks for sentence classification," in Proc. EMNLP, Doha, Qatar, 2014, pp. 1746-1751.
10. A. Vaswani et al., "Attention is all you need," in Advances in Neural Information Processing Systems (NeurIPS), 2017, pp. 5998-6008.
11. J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, "BERT: Pre-training of deep bidirectional transformers for language
understanding," in Proc. NAACL-HLT, 2019, pp. 4171-4186.
12. F. Pedregosa et al., "Scikit-learn: Machine learning in Python," Journal of Machine Learning Research, vol. 12, pp. 2825-2830, 2011.
13. D. Jurafsky and J. H. Martin, Speech and Language Processing, 3rd ed. Upper Saddle River, NJ: Pearson, 2020.
14. S. Hochreiter and J. Schmidhuber, "Long short-term memory," Neural Computation, vol. 9, no. 8, pp. 1735-1780, 1997.
15. Anti-Phishing Working Group (APWG), "Phishing Activity Trends Report, Q4 2023," APWG, Tech. Rep., 2024.
16. R. Basnet, S. Mukkamala, and A. H. Sung, "Detection of phishing attacks: A machine learning approach," in Soft Computing
Applications in Industry, vol. 226, 2008, pp. 373-383.
17. T. Moore and R. Clayton, "Examining the impact of website take-down on phishing," in Proc. APWG eCrime Researchers Summit,
2007.
18. G. Salton and M. J. McGill, Introduction to Modern Information Retrieval. New York: McGraw-Hill, 1983.
19. Z. Zhang, J. Ma, J. Han, and X. Niu, "Email phishing detection by analysing natural language characteristics," in Proc. IEEE CCECE,
2007.
20. K. L. Chiew, K. S. C. Yong, and C. L. Tan, "A survey of phishing attacks: Their types, vectors and technical approaches," Expert
Systems with Applications, vol. 106, pp. 1-20, 2018.
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