Keystroke Dynamics authentication using Data Fiitering Techniques and Neurai Network Approaches
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Date
2010
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Abstract
bcuring the secret data and computer systems by allowing
ase access only to the authenticated users and enduring the
^ k s of imposters is one of the major challenges in the
of computer security. Traditionally, user name and
■sssvord schemes are widely used for controlling the access
B computer systems. But, this scheme has many flaws such
B Password sharing. Shoulder surfing. Brute force attack,
ISctionary attack, Guessing, Phishing and many more.
Sametrics technologies provide more reliable and efficient
»52ns of authentication and verification. Keystroke
S^namics is one of the famous biometric technologies,
i ^ h will try to identify the authentication of a user when
be user is working with a keyboard. In this paper, neural
Wnork approaches with keystrokes for three different
jBss>N'ords namely weak, medium and strong passwords are
Bten into consideration. Neural Network algorithms are
azended by applying normalization techniques for data
fflsring before performing the classification on the datasets.
iSe performance of normalization based neural network
^ rithm s is compared against neural network algorithms
all different category of passwords and the accuraey
lijtained is compared.