JPR_V2_N2_RevP1 Md Liakat Ali Kutub Thakur Charles C. Tappert Journal on Pattern Recognition 2350-112X 2 2 34 45 Authentication, Identification, keystroke Dynamic, Neural Network, Comparison Now-a-days people are heavily dependent on computers to store and process important information. User authentication and identification has become one of the most important and challenging issue in order to secure them from intruders. As traditional user ID and password scheme have failed to provide information security, keystroke dynamics authentication systems can be used to strengthen the existing security techniques. Keystroke dynamic authentication systems are transparent, low cost, and non-invasive for the user, but it has lower accuracy and lower performance compared to other biometric authentication systems. The aim of this paper is to depict a detailed survey of the researches on keystroke dynamic authentication that have used neural networks for classification described in the last two decades. The summary, accuracy of each experiment, and shortcomings of those researches have been presented in this study. Finally, the paper addresses some challenges in keystroke dynamic authentication systems using neural networks that need to be resolve in order to get better performance. June - August 2015 Copyright © 2015 i-manager publications. All rights reserved. i-manager Publications http://www.imanagerpublications.com/Article.aspx?ArticleId=3567