10.57647/cna.2025.rm02-9s74

Automated Phishing Detection Through URL Analysis and Machine Learning

PDF
  • Vijay Kumar*1,
  • G N Basavaraj 1,
  1. Department of Information Science and Engineering, BMS Institute Of Technology and Management, Bangalore, India

Received: 2024-12-15

Revised: 2025-02-20

Accepted: 2025-03-22

Published in Issue 2025-06-30

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

Automated Phishing Detection Through URL Analysis and Machine Learning. (2025). Communications in Nonlinear Analysis, 13(1), 21-25. https://doi.org/10.57647/cna.2025.rm02-9s74

PDF views: 7

Abstract

Phishing attacks are categorized as one of the greatest threats to cybersecurity. They are a form of misinfor-mation designed to make users provide important and personal information via fake websites or emails. This paper realizes the notion of a machine learning-based phishing detection tool aimed at classifying URLs as ”phishing,” ”suspicious,” or ”safe.” Utilizing a Random Forest classifier, the system examines URL-based characteristics, including URL length, special symbols, and the usage of HTTPS to distinguish between real URLs and fake ones (phishing URLs) with high accuracy. The model was trained and validated on a dataset of labeled URLs, achieving 95.2% classification accuracy, which is higher compared to other results. For the sake of usability, the detection tool is implemented as a web application for real-time classification with a user-friendly interface. The performance is evaluated using metrics such as accuracy, precision, recall, and F1-score to assess effectiveness. This paper helps to improve the level of online security by providing an automated approach that reduces dependence on human judgment and can efficiently detect phishing threats.

Keywords

  • Phishing detection,
  • Machine learning; URL classification,
  • Random Forest,
  • Cybersecurity,
  • Real-time detection,
  • Web application
PDF

References

  1. A. Soni and P. Abrol. Phishing website detection. Master’s thesis, Jaypee University of Information Technology, Waknaghat, India, 2022. Bachelor of Technology thesis, supervised by Mr. Prateek Thakral.
  2. H. H. Nguyen and D. T. Nguyen. An intelligent system for detecting phishing websites using machine learning. In Proceedings of the 2019 IEEE International Conference on Cybersecurity and Resilience (ICCSR), pages 1–6. IEEE, 2019. DOI: https://doi.org/10.1109/CAIS.2019.8769571.
  3. T. Choudhary, S. Mhapankar, R. Buddha, Kharuk, and R. Patil. A machine learning approach for phishing attack detection. Journal of Artificial Intelligence and Technology, 3(3): 108–113, 2023. DOI: https://doi.org/10.37965/jait.2023.0197.
  4. S. Hossain, D. Sarma, and R. Chakma. Machine learning-based phishing attack detection. International Journal of Advanced Computer Science and Applications (IJACSA), 11(9): 378–388, 2020. DOI: https://doi.org/10.14569/IJACSA.2020.0110949.
  5. G. Mohamed, J. Visumathi, M. Mahdal, J. Anand, and M. Elangovan. An effective and secure mechanism for phishing attacks using a machine learning approach. Processes, 10(7):1356, 2022. DOI: https://doi.org/10.3390/pr10071356.
  6. R. Mahajan and I. Siddavatam. Phishing website detection using machine learning algorithms. International Journal of Computer Applications, 181(23), 2018. DOI: https://doi.org/10.5120/ijca2018918026.
  7. J. Rashid, T. Nazir, T. Mahmood, and M. W. Nisar. Phishing detection using machine learning technique. In 2020 First International Conference of Smart Systems and Emerging Technologies (SMART-TECH), 2020.
  8. DOI: https://doi.org/10.1109/SMART-TECH49988.2020.00026.
  9. O. K. Sahingoz, E. Buber, O. Demir, and B. Diri. Machine learning based phishing detection from urls. Expert Systems with Applications, 117: 345–357, 2019. DOI: https://doi.org/10.1016/j.eswa.2018.09.029.
  10. A. Safi and S. Singh. A systematic literature review on phishing website detection techniques. Journal of King Saud University - Computer and Information Sciences, 35(5):590–611, 2023. DOI: https://doi.org/10.1016/j.jksuci.2023.01.004.
  11. A. Basit, M. Zafar, X. Liu, A. R. Javed, Z. Jalil, and K. Kifayat. A comprehensive survey of ai-enabled phishing attacks detection techniques. Telecommunication Systems, 76(2):139–154, 2021. DOI: https://doi.org/10.1007/s11235-020-00733-2.