Phishing Site Detection Using ML Algorithms

Abstract

This study delves into the growing threat of online phishing frauds by evaluating the efficacy of diverse machine learning (ML) algorithms in pinpointing malicious websites. Given the substantial risks phishing attacks pose to user privacy and security, it emerges as a promising solution due to its adaptability and the ability to glean insights from extensive datasets. Past research underscores the potential of Support Vector Machines (SVM) and Random Forests in phishing detection. Nevertheless, challenges persist in optimal algorithm selection and feature prioritization. The proposed system integrates Gradient Boosting and Cat Boost alongside Random Forest, leveraging features from the UC Irvine Machine Learning Repository. The study's relevance lies in its performance analysis, which steers the selection of the most effective algorithm for detecting phishing websites, making it pertinent for automated systems addressing the evolving landscape of online threats.