Research Article
A Machine Learning Approach to Identifying Phishing Websites: A Comparative Study of Classification Models and Ensemble Learning Techniques
@ARTICLE{10.4108/eetsis.vi.3300, author={Padma Jyothi Uppalapati and Bhogesh Karthik Gontla and Priyanka Gundu and S Mahaboob Hussain and Kandula Narasimharo}, title={A Machine Learning Approach to Identifying Phishing Websites: A Comparative Study of Classification Models and Ensemble Learning Techniques}, journal={EAI Endorsed Transactions on Scalable Information Systems}, volume={10}, number={5}, publisher={EAI}, journal_a={SIS}, year={2023}, month={6}, keywords={Web Phishing, Classification techniques, Ensemble learning, Machine Learning}, doi={10.4108/eetsis.vi.3300} }- Padma Jyothi Uppalapati
Bhogesh Karthik Gontla
Priyanka Gundu
S Mahaboob Hussain
Kandula Narasimharo
Year: 2023
A Machine Learning Approach to Identifying Phishing Websites: A Comparative Study of Classification Models and Ensemble Learning Techniques
SIS
EAI
DOI: 10.4108/eetsis.vi.3300
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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