An Efficient Approaches for Website Phishing Detection using Supervised Machine Learning Technique

Abstract

Internet has become a useful component of our regular social and financial activities. Internet users may get
harm due to different types of web threats which may cause loss of private information, financial damage , damage
brand reputation due to which it loses customers confidence in E-commerce and online Transaction. Phishing is a form
of web threats that is defined as the art of mimicking a website to illegally acquire and use someone else’s data on behalf
of legitimate website for own benefit (e.g. Steal of user’s password and credit card details during online communication).
So far, there is no single solution that can capture every phishing attack.This paper employs Machine-learning technique
for modelling the prediction task and supervised learning algorithms namely Multi-layer perceptron, Decision tree
induction and Naïve Bayes classification are used for exploring the results.