Authors: Mr. Uppala Haresh, Assistant Professor Mrs. Perla Ratna Kumari
Abstract: Recent years have witnessed a notable rise in phishing attacks targeting websites. Many researchers have developed tools aimed at identifying such fraudulent sites. Nevertheless, these tools are not fully capable of recognizing all threats. There are several minor challenges in detecting fake websites. Therefore, incorporating machine learning techniques into the detection process is the most effective approach. This enhances the overall accuracy of the project. Moreover, it allows for more efficient computation. Utilizing machine learning methods can also help tackle the challenges posed by existing phishing detection models. The main objective of this project is to use the dataset designed to train the ENASSEMBLE Machine Learning (ML) model for identifying phishing websites.
