KUNAL KHADILKAR

Abstract:

Phishing is described as the process of mimicking a company’s website in order to gain vital information from the users. Email phishing leads to severe data loss and may harm the privacy of the end user as well as of the system. Many features have been identified to detect phishing in order to mitigate its effect before it occurs. In this project, we present a comparative analysis of different classifiers for predicting Email Phishing. Apart from Logistic Regression, Decision trees, etc. we have used Ensemble models to predict Phishing with a better accuracy and precision.  Along with it, we have also used the novel approach of the new Wide and Deep Neural Network to detect Phishing.