A Machine Learning Approach to Identifying Phishing Websites: A Comparative Study of Classification Models and Ensemble Learning Techniques

Authors

DOI:

https://doi.org/10.4108/eetsis.vi.3300

Keywords:

Web Phishing, Classification techniques, Ensemble learning, Machine Learning

Abstract

Phishing assaults are one of the more prevalent types of cybercrime in the world today. To steal information, users are sent emails and messages. Moreover, websites are used for it. Phishing primarily targets corporate web-sites, such as those for e-commerce, finance, and governmental organizations. In order to obtain sensitive user information, attackers impersonate websites, a phenomenon known as phishing. In addition to exploring the use of machine learning algorithms to identify and stop web phishing assaults, this research suggests utilizing machine learning techniques to detect phish-ing URLs by analysing various aspects of the URLs. The study includes classification models like Logistic Regression, Random Forest, Decision trees, KNN, Naive bayes, SVM and other ensemble learning techniques like Gradient Boosting, XGBoost, Histogram Gradient Boosting, Light Gradient Boosting and AdaBoost were used to detect phishing websites.

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Published

23-06-2023

How to Cite

1.
Uppalapati PJ, Gontla BK, Gundu P, Hussain SM, Narasimharo K. A Machine Learning Approach to Identifying Phishing Websites: A Comparative Study of Classification Models and Ensemble Learning Techniques. EAI Endorsed Scal Inf Syst [Internet]. 2023 Jun. 23 [cited 2024 Jul. 22];10(5). Available from: https://publications.eai.eu/index.php/sis/article/view/3300