A Framework for Predicting Phishing Websites using Neural Networks

TL;DR

This paper proposes a neural network-based framework to improve the detection and prediction of phishing websites, addressing a critical security issue in online banking in India.

Contribution

It introduces a novel neural network framework specifically designed for classifying and predicting phishing websites, enhancing detection accuracy.

Findings

Neural network framework improves phishing detection accuracy.

The approach effectively classifies phishing versus legitimate websites.

The method addresses the dynamic nature of phishing threats.

Abstract

In India many people are now dependent on online banking. This raises security concerns as the banking websites are forged and fraud can be committed by identity theft. These forged websites are called as Phishing websites and created by malicious people to mimic web pages of real websites and it attempts to defraud people of their personal information. Detecting and identifying phishing websites is a really complex and dynamic problem involving many factors and criteria. This paper discusses about the prediction of phishing websites using neural networks. A neural network is a multilayer system which reduces the error and increases the performance. This paper describes a framework to better classify and predict the phishing sites using neural networks.