Phishing Website Detection Using Advanced Machine Learning Techniques

Authors

  • Nitin N. Sakhare BRACT’s Vishwakarma Institute of Information Technology, Pune
  • Jyoti L. Bangare MKSSS’s Cummins College of Engineering, Pune
  • Radhika G. Purandare BRACT’s Vishwakarma Institute of Information Technology, Pune
  • Disha S. Wankhede BRACT’s Vishwakarma Institute of Information Technology, Pune
  • Pooja Dehankar Assistant Professor School of Engineering, Ajeenkya D. Y. Patil University, Pune

Keywords:

Phishing Detection, Artificial Intelligence (AI), URL Analysis, XGBoost, LightGBM, Naïve Bayes, CatBoost, Graph Neural Network (GNN), Feature Extraction, Real-time Monitoring

Abstract

In the contemporary digital landscape, the escalating threat of phishing attacks necessitates innovative solutions for timely detection and mitigation. This paper presents a pioneering endeavour at the intersection of Artificial Intelligence (AI) and Machine Learning (ML) to combat phishing attempts. Employing a multifaceted approach, this research work integrates XGBoost, LightGBM, Naïve Bayes and CatBoost algorithms, alongside a Graph Neural Network (GNN), to meticulously analyse URL structures, content patterns, and user behaviour. Features such as URL length, dots, slashes, numbers, and special characters are extracted for comprehensive model training. Real-time monitoring ensures the continual adaptation of the system to emerging phishing tactics, enhancing its efficacy in proactively safeguarding users and organizations from the dynamic and evolving realm of cyber threats. This research encapsulates a comprehensive exploration of diverse machine learning methodologies to fortify online security against the pervasive threat of phishing.

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Published

12.01.2024

How to Cite

Sakhare, N. N. ., Bangare, J. L. ., Purandare, R. G. ., Wankhede, D. S. ., & Dehankar, P. . (2024). Phishing Website Detection Using Advanced Machine Learning Techniques. International Journal of Intelligent Systems and Applications in Engineering, 12(12s), 329 –. Retrieved from https://www.ijisae.org/index.php/IJISAE/article/view/4519

Issue

Vol. 12 No. 12s (2024)

Section

Research Article

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