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Original Article
Phishing Detection Using Behavioral Cues in Browser Interaction
Shanmathi K1
Dr. S. Latha2
1 MSc student, cyber forensic and Information Security, Dr MGR Educational and Research Institute, Chennai, Tamil Nadu, India. 2 Director i/c, center for cyber forensics and information security, University of madras, Chennai, Tamil Nadu, India.
Published Online: May-August 2026
Pages: 247-252
Cite this article
↗ https://www.doi.org/10.59256/indjcst.20260502028
References
1) Ahmed, M., & Traore, I. (2017). A new biometric technology based on mouse dynamics. IEEE Transactions on Dependable and Secure
Computing.
2) Aljofey, A., Jiang, Q., Rasool, A., Chen, H., & Liu, W. (2020). An effective phishing detection model based on character-level convolutional
neural network. Electronics, 9(9).
3) Anti-Phishing Working Group. (2024). Phishing activity trends report. https://apwg.org
4) Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32.
5) Egele, M., Kirda, E., Kruegel, C., & Vigna, G. (2008). Phishing detection using machine learning. In NDSS Symposium Proceedings.
6) Feng, T., Liu, W., & Deng, X. (2018). Detecting phishing websites using machine learning techniques. Journal of Information Security.
7) Google. (2023). Google Safe Browsing API documentation. https://developers.google.com
8) Ho, T. K. (1995). Random decision forests. In Proceedings of the International Conference on Document Analysis and Recognition.
9) Killourhy, K. S., & Maxion, R. A. (2009). Comparing anomaly detection algorithms for keystroke dynamics. In Proceedings of the IEEE/IFIP
International Conference on Dependable Systems and Networks.
10) Open Web Application Security Project (OWASP). (2023). Phishing attack prevention guidelines. https://owasp.org
11) Statista. (2024). Global phishing statistics and cybercrime reports. https://www.statista.com
12) Verma, R., & Hossain, N. (2017). Semantic feature selection for text-based phishing detection. In Proceedings of the IEEE Conference on
Communications and Network Security.
13) 13. M. Rahman and H. Arif, "A Machine Learning Approach for Detecting Phishing Websites," Int. J. of Computer Applications, vol. 176,
no. 1, pp. 25–32, 2020.
14) R. M. Mohammad and F. Thabtah, "Phishing Website Detection Using Hybrid Machine Learning Techniques," J. of Information Security
and Applications, vol. 58, 2021, Art. no. 102729.
15) Y. Chen and X. Wang, "Detecting Phishing Attacks Using User Browsing Behavior," IEEE Access, vol. 10, pp. 12345–12356, 2022.
16) A. Alqahtani and K. Alshamrani, "Deep Learning-Based Phishing Detection Using Web Interaction Data," J. of Cybersecurity and Digital
Forensics, vol. 8, no. 2, pp. 45–58, 2023.
17) P. Singh and A. Kumar, "Behavior-Based Phishing Detection in Web Browsers," Int. J. of Computer Networks and Applications, vol. 11,
no. 3, pp. 67–79, 2024.
18) L. Pereira and M. Silva, "Phishing Detection Using Mouse Dynamics and Keystroke Behavior," J. of Information Security, vol. 12, no. 4,
pp. 89–102, 2021.
Computing.
2) Aljofey, A., Jiang, Q., Rasool, A., Chen, H., & Liu, W. (2020). An effective phishing detection model based on character-level convolutional
neural network. Electronics, 9(9).
3) Anti-Phishing Working Group. (2024). Phishing activity trends report. https://apwg.org
4) Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32.
5) Egele, M., Kirda, E., Kruegel, C., & Vigna, G. (2008). Phishing detection using machine learning. In NDSS Symposium Proceedings.
6) Feng, T., Liu, W., & Deng, X. (2018). Detecting phishing websites using machine learning techniques. Journal of Information Security.
7) Google. (2023). Google Safe Browsing API documentation. https://developers.google.com
8) Ho, T. K. (1995). Random decision forests. In Proceedings of the International Conference on Document Analysis and Recognition.
9) Killourhy, K. S., & Maxion, R. A. (2009). Comparing anomaly detection algorithms for keystroke dynamics. In Proceedings of the IEEE/IFIP
International Conference on Dependable Systems and Networks.
10) Open Web Application Security Project (OWASP). (2023). Phishing attack prevention guidelines. https://owasp.org
11) Statista. (2024). Global phishing statistics and cybercrime reports. https://www.statista.com
12) Verma, R., & Hossain, N. (2017). Semantic feature selection for text-based phishing detection. In Proceedings of the IEEE Conference on
Communications and Network Security.
13) 13. M. Rahman and H. Arif, "A Machine Learning Approach for Detecting Phishing Websites," Int. J. of Computer Applications, vol. 176,
no. 1, pp. 25–32, 2020.
14) R. M. Mohammad and F. Thabtah, "Phishing Website Detection Using Hybrid Machine Learning Techniques," J. of Information Security
and Applications, vol. 58, 2021, Art. no. 102729.
15) Y. Chen and X. Wang, "Detecting Phishing Attacks Using User Browsing Behavior," IEEE Access, vol. 10, pp. 12345–12356, 2022.
16) A. Alqahtani and K. Alshamrani, "Deep Learning-Based Phishing Detection Using Web Interaction Data," J. of Cybersecurity and Digital
Forensics, vol. 8, no. 2, pp. 45–58, 2023.
17) P. Singh and A. Kumar, "Behavior-Based Phishing Detection in Web Browsers," Int. J. of Computer Networks and Applications, vol. 11,
no. 3, pp. 67–79, 2024.
18) L. Pereira and M. Silva, "Phishing Detection Using Mouse Dynamics and Keystroke Behavior," J. of Information Security, vol. 12, no. 4,
pp. 89–102, 2021.
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