A Framework for Predicting Phishing Websites using Neural Networks

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.

A hybrid model for bankruptcy prediction using genetic algorithm, fuzzy c-means and mars

Bankruptcy prediction is very important for all the organization since it affects the economy and rise many social problems with high costs. There are large number of techniques have been developed to predict the bankruptcy, which helps the decision makers such as investors and financial analysts. One of the bankruptcy prediction models is the hybrid model using Fuzzy C-means clustering and MARS, which uses static ratios taken from the bank financial statements for prediction, which has its own theoretical advantages. The performance of existing bankruptcy model can be improved by selecting the best features dynamically depend on the nature of the firm. This dynamic selection can be accomplished by Genetic Algorithm and it improves the performance of prediction model.