Abstract
Biometric authentication is individual characteristics that cannot be used by imposter to penetrate secure system. Keystroke dynamics based authentication verifies user from their typing pattern. To authenticate user based on their typing samples, it is required to find out he resemblance of a typing samples of user regardless of the text typed. Key event timing is extracted from key features Latency, Dwell time, Key interval, Up to up, Flight time and standard are measure in the form of FAR, FRR and ER. In this paper we introduces a k-nearest neighbor approach to classify users’ keystroke dynamics profiles. For authentication, an input will be checked against the profiles within the cluster which has significantly reduced the verification load.
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Dholi, P.R., Chaudhari, K.P. (2013). Typing Pattern Recognition Using Keystroke Dynamics. In: Das, V.V., Chaba, Y. (eds) Mobile Communication and Power Engineering. AIM 2012. Communications in Computer and Information Science, vol 296. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35864-7_39
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DOI: https://doi.org/10.1007/978-3-642-35864-7_39
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-35863-0
Online ISBN: 978-3-642-35864-7
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