Phishing Website Detection Using Machine Learning

Abstract

Phishing involves a deceptive online tactic where an individual pretends to be trustworthy, aiming to deceive others into revealing sensitive information. An exemplary instance is trolling, which has posed an ongoing difficulty. Thankfully, recent progress in identifying phishing attempts, particularly through the application of machine learning, has led to a decrease in such occurrences. This research delves into constructing and contrasting four models to gauge machine learning's effectiveness in spotting phishing websites. Moreover, we evaluate the top-performing model against established methods documented in published studies. These models make use of (SVMs), (DTs) & (RF) .Our results indicate that the Random Forest model stands out as the most accurate among the four methods, surpassing them in precision, accuracy, and overall performance.