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

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.

ML and DL-based Phishing Website Detection: The Effects of Varied Size Datasets and Informative Feature Selection Techniques

“…Despite the use of DS-1, the CATB accuracy on DS-1 was 1.07 percent better than the best accuracy (96.83 percent) achieved by the study [27] and 0.85 percent better than the best accuracy (97.05 percent) achieved by the study [45]. Despite using DS-2, the CATB accuracy in DS-3 (98.83%) was 4.23% higher than the best accuracy (94.6%) achieved by RF in the study [22], 0.83% higher than the best accuracy (98%) achieved by RF in the study [46], and 1% higher than the best accuracy (97.83%) achieved by [45].…”