Deep learning-based phishing classification framework for accurate detection using optimized URL intelligence – Scientific Reports

1. Butt, M. H. F., Li, J. P., Saboor, T., Arslan, M. & Butt, M. A. F. Intelligent Phishing Url Detection: A Solution Based On Deep Learning Framework. In 2021 18th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP) (pp. 434–439). IEEE. (2021), December.

2. Hosseinzadeh, M., Ali, U., Ali, S., Abbaszadi, R., Gharehchopogh, F. S., Khoshvaght,P., … Lansky, J. (2025). Improving phishing email detection performance through deep learning with adaptive optimization. Scientific Reports, 15(1), 36724.

3. Mourtaji, Y., Bouhorma, M., Alghazzawi, D., Aldabbagh, G. & Alghamdi, A. Hybrid Rule-Based Solution for Phishing URL Detection Using Convolutional Neural Network. Wirel. Commun. Mob. Comput. 2021 (1), 8241104 (2021).

   Google Scholar 

4. Ramkumar, G. Elevated Learning based Secured Phishing Website Identification Methodology using Artificial Intelligence Assistance. In 2024 5th International Conference on Electronics and Sustainable Communication Systems (ICESC) (pp. 1543–1551). IEEE. (2024), August.

5. Tang, L. & Mahmoud, Q. H. A deep learning-based framework for phishing website detection. IEEe Access. 10, 1509–1521 (2021).

   Google Scholar 

6. Abad, S., Gholamy, H. & Aslani, M. Classification of malicious URLs using machine learning. Sensors 23 (18), 7760 (2023).

   Google Scholar 

7. Abu Al-Haija, Q. & Al-Fayoumi, M. An intelligent identification and classification system for malicious uniform resource locators (URLs). Neural Comput. Appl. 35 (23), 16995–17011 (2023).

   Google Scholar 

8. Balogun, A. O. et al. Optimized decision forest for website phishing detection. In Proceedings of the Computational Methods in Systems and Software (pp. 568–582). Cham: Springer International Publishing. (2021).

9. Piñeiro, J. L. & Portillo, L. W. Web architecture for URL-based phishing detection based on Random Forest, Classification Trees, and Support Vector Machine. Inteligencia Artif. 25 (69), 107–121 (2022).

   Google Scholar 

10. Ahmad, S. et al. Across the spectrum in-depth review AI-based models for phishing detection. IEEE Open. J. Commun. Soc. 6, 2065–2089 (2024).

    Google Scholar 

11. Prasad, A. & Chandra, S. PhiUSIIL: A diverse security profile empowered phishing URL detection framework based on similarity index and incremental learning. Computers Secur. 136, 103545 (2024).

    Google Scholar 

12. Essien, I. A. et al. Neural network-based phishing attack detection and prevention systems. J. Front. Multidisciplinary Res. 2 (2), 222–238 (2021).

    Google Scholar 

13. Aljofey, A. et al. An effective detection approach for phishing websites using URL and HTML features. Sci. Rep. 12 (1), 8842 (2022).

    Google Scholar 

14. Sánchez-Paniagua, M., Fernández, E. F., Alegre, E., Al-Nabki, W. & González-Castro, V. Phishing URL detection: A real-case scenario through login URLs. IEEe Access. 10, 42949–42960 (2022).

    Google Scholar 

15. Asiri, S., Xiao, Y., Alzahrani, S., Li, S. & Li, T. A survey of intelligent detection designs of HTML URL phishing attacks. IEEe Access. 11, 6421–6443 (2023).

    Google Scholar 

16. https://www.kaggle.com/datasets/kaggleprollc/phishing-url-websites-dataset-phiusiil

17. Radhakrishnan Subramaniam, P., Palakeel, M., Arunmozhi, M., Sridharan, U. & Marimuthu Factors driving business intelligence adoption: an extended technology-organization-environment framework, Indonesian Journal of Electrical Engineering and Computer Science Vol. 34, No. 3, June 2024, p. 1893 ~ 1903 ISSN: 2502–4752. https://doi.org/10.11591/ijeecs.v34.i3.pp1893-1903

18. Vetriselvi, D. & Thenmozhi, R. Advanced image processing techniques for ultrasound images using multiscale self attention CNN. Neural Process. Lett. 55 (9), 11945–11973 (2023).

    Google Scholar 

19. Manikandan, S. & Elakiya, E. Efficient energy consumption in hybrid cloud environment using adaptive backtracking virtual machine consolidation. Sci. Rep. 14, 22869 (2024).

    Google Scholar 

20. Alotaibi, S. R., Alkahtani, H. K., Aljebreen, M., Alshuhail, A., Saeed, M. K., Ebad,S. A., … Alotaibi, M. (2025). Explainable artificial intelligence in web phishing classification on secure IoT with cloud-based cyber-physical systems. Alexandria Engineering Journal, 110, 490–505.

21. Alshahrani, S. M., Khan, N. A., Almalki, J. & Shehri, A. W. URL Phishing Detection Using Particle Swarm Optimization and Data Mining. Computers Mater. & Continua, 73(3). (2022).

22. Abdul Samad, S. R., Balasubaramanian, S., Al-Kaabi, A. S., Sharma, B., Chowdhury,S., Mehbodniya, A., … Bostani, A. (2023). Analysis of the performance impact of fine-tuned machine learning model for phishing URL detection. Electronics, 12(7), 1642.

23. Almomani, O. et al. Enhance URL defacement attack detection using particle swarm optimization and machine learning. J. Comput. Cogn. Eng. 4 (3), 296–308 (2025).

    Google Scholar 

24. Alsenani, T. R., Ayon, S. I., Yousuf, S. M., Anik, F. B. K. & Chowdhury, M. E. S. Intelligent feature selection model based on particle swarm optimization to detect phishing websites. Multimedia Tools Appl. 82 (29), 44943–44975 (2023).

    Google Scholar 

25. Alsaidi, S. A. A. A. et al. D., HawkPhish-DNN cybersecurity model: adaptive hybrid optimization and deep learning for enhanced multi-objective phishing URL detection. International J. Inform. Technology, 1–17. (2025).

26. Kumar, S. S., Muthusamy, P. & Jerald, M. P. A. A Hybrid Framework for Improved Weighted Quantum Particle Swarm Optimization and Fast Mask Recurrent CNN to Enhance Phishing-URL Prediction Performance. Int. J. Comput. Intell. Syst. 17 (1), 251 (2024).

    Google Scholar 

27. Jalil, S., Usman, M. & Fong, A. Highly accurate phishing URL detection based on machine learning. J. Ambient Intell. Humaniz. Comput. 14 (7), 9233–9251 (2023).

    Google Scholar 

28. Remya, S., Pillai, M. J., Nair, K. K., Subbareddy, S. R. & Cho, Y. Y. An effective detection approach for phishing URL using ResMLP. IEEE access. 12, 79367–79382 (2024).

    Google Scholar 

29. Anupam, S. & Kar, A. K. Phishing website detection using support vector machines and nature-inspired optimization algorithms. Telecommunication Syst. 76 (1), 17–32 (2021).

    Google Scholar 

30. Ghaleb Al-Mekhlafi, Z., Abdulkarem Mohammed, B., Al-Sarem, M., Saeed, F., Al-Hadhrami,T., Alshammari, M. T., … Sarheed Alshammari, T. (2022). Phishing websites detection by using optimized stacking ensemble model. Computer Systems Science and Engineering, 41(1), 109–125.

31. Nowroozi, E., Mohammadi, M., Rahdari, A., Taheri, R. & Conti, M. A random deep feature selection approach to mitigate transferable adversarial attacks. IEEE Trans. Netw. Service Management (2025).

32. Taheri, R., Ahmadzadeh, M. & Kharazmi, M. R. A new approach for feature selection in intrusion detection system. Fen Bilimleri Dergisi (CFD). 36 (6), 1344–1357 (2015).

    Google Scholar 

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