DETECTION OF PHISHING WEBSITES USING MACHINE LEARNING ALGORITHMS

As the internet becomes an integral part of daily life, the threat of phishing attacks has surged, jeopardizing users' sensitive information. This project focuses on leveraging machine learning algorithms to detect phishing websites effectively. By employing a dataset comprising features extracted from the structure and content of known phishing and legitimate websites, our model aims to discern subtle patterns indicative of malicious intent. Through the application of supervised learning techniques, such as decision trees and ensemble methods, we achieve high accuracy in differentiating between phishing and legitimate websites. The proposed approach not only enhances current phishing detection capabilities but also contributes to the ongoing efforts to fortify cyber security in the ever-evolving digital landscape.