Mohammad, Rami, McCluskey, T.L. and Thabtah, Fadi (2013) Predicting Phishing Websites using Neural Network trained with Back-Propagation. In: Proceedings of the 2013 World Congress in Computer Science, Computer Engineering, and Applied Computing. WORLDCOMP 2013 . World Congress in Computer Science, Computer Engineering, and Applied Computing, Las Vegas, Nevada, USA, pp. 682-686. ISBN 1601322461

Phishing is increasing dramatically with the development of modern technologies and the global worldwide computer networks. This results in the loss of customer’s confidence in e-commerce and online banking, financial damages, and identity theft. Phishing is fraudulent effort aims to acquire sensitive information from users such as credit card credentials, and social security number. In this article, we propose a model for predicting phishing attacks based on Artificial Neural Network (ANN). A Feed Forward Neural Network trained by Back Propagation algorithm is developed to classify websites as phishing or legitimate. The suggested model shows high acceptance ability for noisy data, fault tolerance and high prediction accuracy with respect to false positive and false negative rates.

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