Phishing Website Detection Using Machine Learning

Authors

  • Mowafaq Salem Alzboon Jadara University, Faculty of Information Technology. Irbid, Jordan Author https://orcid.org/0000-0002-3522-6689
  • Mohammad Subhi Al-Batah Jadara University, Faculty of Information Technology. Irbid, Jordan Author https://orcid.org/0000-0002-9341-1727
  • Muhyeeddin Alqaraleh Zarqa University, Faculty of Information Technology. Zarqa, Jordan Author https://orcid.org/0009-0001-9103-2002
  • Faisal Alzboon Caucasus International University (CIU), Dental Medicine, Tbilisi, Georgia Author
  • Lujin Alzboon Caucasus International University (CIU), Dental Medicine, Tbilisi, Georgia Author

DOI:

https://doi.org/10.56294/gr202581

Keywords:

Phishing, Website Detection, Machine Learning, Feature Extraction, Cybersecurity

Abstract

Phishing attacks continue to be a danger in our digital world, with users being manipulated via rogue websites that trick them into disclosing confidential details. This article focuses on the use of machine learning techniques in the process of identifying phishing websites. In this case, a study was undertaken on critical factors such as URL extension, age of domain, and presence of HTTPS whilst exploring the effectiveness of Random Forest, Gradient Boosting and, Support Vector Machines algorithms in allocating a status of phishing or non-phishing. In this study, a dataset containing real URLs and phishing URLs are employed to build the model using feature extraction. Following this, the various algorithms were put to the test on this dataset; out of all the models, Random Forest performed exceptionally well having achieved an accuracy of 97.6%, Gradient Boosting was also found to be extremely effective possessing strong accuracy and accuracy. In this study we also compared and discussed methods to detect a phishing site. Some features that affect detection performance include URL length, special characters and the focus on even more aspects that need further development. The new proposed method improves the detection accuracy of the phishing websites because machine learning techniques are applied, recall (true positive) increase, while false positive decrease. The results enrich the electronic security system, as they enable effective detection in real time mode. This study has demonstrated the importance of employing cutting-edge techniques to deal with phishing attacks and safeguard users against advanced cyber threats, thus laying the groundwork for innovation in phishing detection systems in the future

References

1. Al-batah M, Al-Batah M, Salem Alzboon M, Alzaghoul E. Automated Quantification of Vesicoureteral Reflux using Machine Learning with Advancing Diagnostic Precision. Data Metadata [Internet]. 2025 Jan 1;4:460. Available from: http://dx.doi.org/10.56294/dm2025460

2. Abdel Wahed M, Alqaraleh M, Salem Alzboon M, Subhi Al-Batah M. Application of Artificial Intelligence for Diagnosing Tumors in the Female Reproductive System: A Systematic Review. Multidiscip [Internet]. 2025 Jan;3:54. Available from: http://dx.doi.org/10.62486/agmu202554

3. Al-batah M, Al-Batah M, Salem Alzboon M, Alzaghoul E. Automated Quantification of Vesicoureteral Reflux using Machine Learning with Advancing Diagnostic Precision. Data Metadata [Internet]. 2025 Jan 1;4:460. Available from: https://dm.ageditor.ar/index.php/dm/article/view/460

4. Alqaraleh M, Salem Alzboon M, Mohammad SA-B. Optimizing Resource Discovery in Grid Computing: A Hierarchical and Weighted Approach with Behavioral Modeling. LatIA [Internet]. 2025 Jan 1;3:97. Available from: http://dx.doi.org/10.62486/latia202597

5. Wahed MA, Alqaraleh M, Salem Alzboon M, Subhi Al-Batah M. Evaluating AI and Machine Learning Models in Breast Cancer Detection: A Review of Convolutional Neural Networks (CNN) and Global Research Trends. LatIA [Internet]. 2025 Jan 1;3:117. Available from: http://dx.doi.org/10.62486/latia2025117

6. Al-Batah M, Salem Alzboon M, Alqaraleh M. Superior Classification of Brain Cancer Types Through Machine Learning Techniques Applied to Magnetic Resonance Imaging. Data Metadata [Internet]. 2025 Jan 1;4:472. Available from: http://dx.doi.org/10.56294/dm2025472

7. Alzboon MS, Alzboon MS. From Complexity to Clarity: Improving Microarray Classification with Correlation-Based Feature Selection. LatIA. 2025;

8. Retraction: Phishing website detection using machine learning and deep learning techniques (J. Phys.: Conf. Ser. 1916 012169). J Phys Conf Ser [Internet]. 2021 May 1;1916(1):012407. Available from: https://iopscience.iop.org/article/10.1088/1742-6596/1916/1/012407

9. Alqaraleh M, Salem Alzboon M, Subhi Al-Batah M, Solayman Migdadi H. From Complexity to Clarity: Improving Microarray Classification with Correlation-Based Feature Selection. LatIA [Internet]. 2025 Jan 1;3:84. Available from: http://dx.doi.org/10.62486/latia202584

10. Kasim S, Valliani N, Ki Wong NK, Samadi S, Watkins L, Rubin A. Cybersecurity as a Tic-Tac-Toe Game Using Autonomous Forwards (Attacking) And Backwards (Defending) Penetration Testing in a Cyber Adversarial Artificial Intelligence System. In: ICOSNIKOM 2022 - 2022 IEEE International Conference of Computer Science and Information Technology: Boundary Free: Preparing Indonesia for Metaverse Society. 2022.

11. Alqaraleh M, Salem Alzboon M, Subhi Al-Batah M. Real-Time UAV Recognition Through Advanced Machine Learning for Enhanced Military Surveillance. Gamification Augment Real [Internet]. 2025 Jan 1;3:63. Available from: http://dx.doi.org/10.56294/gr202563

12. Baliyan H, Prasath AR. Enhancing Phishing Website Detection Using Ensemble Machine Learning Models. 2024 OPJU Int Technol Conf Smart Comput Innov Adv Ind 40. 2024;

13. Jain M, Rattan K, Sharma D, Goel K, Gupta N. Phishing Website Detection System Using Machine Learning. J Netw Commun Syst. 2024;

14. Pathmanaban J, James PG, Ashok P, Ragesh B, Aakash S, Kaushik N. Phishing Website Detection Using Machine Learning. Proc 2nd IEEE Int Conf Netw Commun 2024, ICNWC 2024. 2024;

15. U S SS. Phishing Website Detection using Machine Learning. Interantional J Sci Res Eng Manag. 2024;08(06):1–5.

16. Alazaidah R, Al-Shaikh A, AL-Mousa MR, Khafajah H, Samara G, Alzyoud M, et al. Website Phishing Detection Using Machine Learning Techniques. J Stat Appl Probab [Internet]. 2024 Jan 1;13(1):119–29. Available from: https://digitalcommons.aaru.edu.jo/jsap/vol13/iss1/8/

17. Wahed MA, Alqaraleh M, Alzboon MS, Al-Batah MS. Application of Artificial Intelligence for Diagnosing Tumors in the Female Reproductive System: A Systematic Review. Multidiscip. 2025;3:54.

18. Wahed MA, Alqaraleh M, Alzboon MS, Subhi Al-Batah M, de la Salud R el C, la de la Inteligencia T. AI Rx: Revolutionizing Healthcare Through Intelligence, Innovation, and Ethics. Semin Med Writ Educ [Internet]. 2025 Jan 1;4(35):35. Available from: http://dx.doi.org/10.56294/mw202535

19. Prayogo RD, Alfisyahrin AR, Gambetta W, Karimah SA, Nambo H. An Explainable Machine Learning-Based Phishing Website Detection using Gradient Boosting. In: Proceeding - 2024 International Conference on Information Technology Research and Innovation, ICITRI 2024 [Internet]. IEEE; 2024. p. 76–81. Available from: https://ieeexplore.ieee.org/document/10698870/

20. Mahalakshmi S, Meena P, Gopinath MR. Detection of Phishing Website Using Machine Learning. Int J Res Publ Rev. 2024;5(2):1817–21.

21. Carrasco Ramírez JG. AI in Healthcare: Revolutionizing Patient Care with Predictive Analytics and Decision Support Systems. J Artif Intell Gen Sci ISSN3006-4023. 2024;1(1):31–7.

22. Vaishnavi Bhoyar, Komal Dharak, Dipali Gawali. Detection of Phishing Website using Machine Learning. Int J Adv Res Sci Commun Technol [Internet]. 2024 Jan 7;26–7. Available from: http://ijarsct.co.in/Paper15004.pdf

23. Al saedi M, Abbas Flayh N. Phishing Website Detection Using Machine Learning: A Review. Wasit J Pure Sci. 2023;2(2):270–81.

24. Kashyap S. The Influence of Artificial Intelligence on Cybersecurity. Int J Innov Res Comput Commun Eng. 2024;12(Special Is):13–22.

25. Subashini K, Narmatha V. Phishing Website Detection using Hyper-parameter Optimization and Comparison of Cross-validation in Machine Learning Based Solution. In: 2023 3rd International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2023. 2023.

26. Al-Batah M, Salem Alzboon M, Alqaraleh M, Ahmad Alzaghoul F. Comparative Analysis of Advanced Data Mining Methods for Enhancing Medical Diagnosis and Prognosis. Data Metadata [Internet]. 2024 Oct 29;3(3):83–92. Available from: http://dx.doi.org/10.56294/dm2024.465

27. Al-shanableh N, Alzyoud M, Al-husban RY, Alshanableh NM, Al-Oun A, Al-Batah MS, et al. Advanced Ensemble Machine Learning Techniques for Optimizing Diabetes Mellitus Prognostication: A Detailed Examination of Hospital Data. Data Metadata [Internet]. 2024 Sep 2;3. Available from: http://dx.doi.org/10.56294/dm2024.363

28. Muhyeeddin A, Mowafaq SA, Al-Batah MS, Mutaz AW. Advancing Medical Image Analysis: The Role of Adaptive Optimization Techniques in Enhancing COVID-19 Detection, Lung Infection, and Tumor Segmentation. LatIA [Internet]. 2024 Sep 29;2:74. Available from: http://dx.doi.org/10.62486/latia202474

29. Alqaraleh M, Alzboon MS, Al-Batah MS. Skywatch: Advanced Machine Learning Techniques for Distinguishing UAVs from Birds in Airspace Security. Int J Adv Comput Sci Appl [Internet]. 2024;15(11):1065–78. Available from: http://dx.doi.org/10.14569/IJACSA.2024.01511104

30. Alqaraleh M, Alzboon MS, Al-Batah MS, Abdel Wahed M, Abuashour A, Alsmadi FH. Harnessing Machine Learning for Quantifying Vesicoureteral Reflux: A Promising Approach for Objective Assessment. Int J Online Biomed Eng [Internet]. 2024 Aug 8;20(11):123–45. Available from: https://online-journals.org/index.php/i-joe/article/view/49673

31. Abuashour A, Salem Alzboon M, Kamel Alqaraleh M, Abuashour A. Comparative Study of Classification Mechanisms of Machine Learning on Multiple Data Mining Tool Kits. Am J Biomed Sci Res 2024 [Internet]. 2024;22(1):1. Available from: www.biomedgrid.com

32. Alzboon MS, Bader AF, Abuashour A, Alqaraleh MK, Zaqaibeh B, Al-Batah M. The Two Sides of AI in Cybersecurity: Opportunities and Challenges. In: 2023 International Conference on Intelligent Computing and Next Generation Networks(ICNGN) [Internet]. IEEE; 2023. p. 1–9. Available from: https://ieeexplore.ieee.org/document/10396670/

33. Alzboon MS, Qawasmeh S, Alqaraleh M, Abuashour A, Bader AF, Al-Batah M. Pushing the Envelope: Investigating the Potential and Limitations of ChatGPT and Artificial Intelligence in Advancing Computer Science Research. In: 2023 3rd International Conference on Emerging Smart Technologies and Applications (eSmarTA) [Internet]. IEEE; 2023. p. 1–6. Available from: https://ieeexplore.ieee.org/document/10293294/

34. Alzboon MS, Al-Batah MS. Prostate Cancer Detection and Analysis using Advanced Machine Learning. Int J Adv Comput Sci Appl [Internet]. 2023;14(8):388–96. Available from: http://thesai.org/Publications/ViewPaper?Volume=14&Issue=8&Code=IJACSA&SerialNo=43

35. Alzboon MS, Al-Batah MS, Alqaraleh M, Abuashour A, Bader AFH. Early Diagnosis of Diabetes: A Comparison of Machine Learning Methods. Int J online Biomed Eng. 2023;19(15):144–65.

36. Alzboon MS, Qawasmeh S, Alqaraleh M, Abuashour A, Bader AF, Al-Batah M. Machine Learning Classification Algorithms for Accurate Breast Cancer Diagnosis. In: 2023 3rd International Conference on Emerging Smart Technologies and Applications (eSmarTA) [Internet]. IEEE; 2023. p. 1–8. Available from: https://ieeexplore.ieee.org/document/10293415/

37. Putri AK, Alzboon MS. Doctor Adam Talib’s Public Relations Strategy in Improving the Quality of Patient Service. Sinergi Int J Commun Sci [Internet]. 2023 May 25;1(1):42–54. Available from: https://journal.sinergi.or.id/index.php/ijcs/article/view/19

38. Al-Batah MS, Alzboon MS, Alazaidah R. Intelligent Heart Disease Prediction System with Applications in Jordanian Hospitals. Int J Adv Comput Sci Appl [Internet]. 2023;14(9):508–17. Available from: http://thesai.org/Publications/ViewPaper?Volume=14&Issue=9&Code=IJACSA&SerialNo=54

39. Alzboon MS, Al-Batah M, Alqaraleh M, Abuashour A, Bader AF. A Comparative Study of Machine Learning Techniques for Early Prediction of Diabetes. In: 2023 IEEE Tenth International Conference on Communications and Networking (ComNet) [Internet]. IEEE; 2023. p. 1–12. Available from: https://ieeexplore.ieee.org/document/10366688/

40. Alzboon MS. Survey on Patient Health Monitoring System Based on Internet of Things. Inf Sci Lett [Internet]. 2022 Jul 1;11(4):1183–90. Available from: https://www.naturalspublishing.com/Article.asp?ArtcID=25233.

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Published

2025-01-16

How to Cite

1.
Alzboon MS, Subhi Al-Batah M, Alqaraleh M, Alzboon F, Alzboon L. Phishing Website Detection Using Machine Learning. Gamification and Augmented Reality [Internet]. 2025 Jan. 16 [cited 2025 Feb. 5];3:81. Available from: https://gr.ageditor.ar/index.php/gr/article/view/81