User Authentication and Identification Using NeuralNetwork

Md Liakat Ali*, Kutub Thakur**, Charles C. Tappert***
*-** Ph.D Scholar, Department of Computer Science and Engineering, Pace University, Newyork.
*** Professor, Seidenberg School of CSIS, Newyork.
Periodicity:June - August'2015


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.


Authentication, Identification, keystroke Dynamic, Neural Network, Comparison.

How to Cite this Article?

Ali, M. L., Thakur, K., and Tappert, C. C. (2015). User Authentication and Identification Using Neural Network. i-manager’s Journal on Pattern Recognition, 2(2), 34-45.


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