Improved Phishing Detection using Ensemble Models in Machine Learning

Abstract

Abstract: Phishing attack is one of the simplest ways to obtain sensitive information from innocent users who are unaware. The main motive of the phishers is to acquire critical information like username, password and bank account details etc. using a fake link which actually looks like a genuine one from an authorized source. Users who have good technical knowledge might be able to identify these links easily but for naive users, this is going to be dangerous as it might lead to loss of privacy and assets. There are other techniques of spam detection based on the sender’s information and content-based detection. In this paper we follow an efficient approach in which we can directly identify an URL if it is Phishing or not by its features using Machine Learning. The aim of this paper is to build multiple Machine Learning models, compare their performances and narrow down to best model based on its performance and efficiency.