User Authentication based on Keystroke Dynamics Using Backpropagation Network
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Abstract
Computer systems and networks are being used in almost every aspect of our daily life; as a result the security threats to computers
and networks have also increased significantly. Traditionally, password-based user authentication is widely used to authenticate legitimate user
in the current system0T but0T this method has many loop holes such as password sharing, shoulder surfing, brute force attack, dictionary attack,
guessing, phishing and many more.
The aim of this paper is to enhance the password authentication method by presenting a keystroke dynamics with back propagation neural
network as a transparent layer of user authentication. Keystroke Dynamics is one of the famous and inexpensive behavioral biometric
technologies, which identifies a user based on the analysis of his/her typing rhythm.
This paper utilizes keystroke features including dwell time (DT), flight time (FT), up-up time (UUT), and a mixture of theses features as
keystroke representation. The back propagation neural network is trained with the mean of keystroke timing information for each character of
password. These times are used to discriminate between the authentic users and impostors.
Results of the experiments demonstrate that the backpropagation network with UUT features comparable to combination of DT and FT. Also,
the results of backpropagation with combination of DT, FT and UUT provide low False Alarm Rate (FAR) and False Reject Rate (FRR) and
high accuracy.
Keywords: Back propagtion, Biometrics, Dwell Time, Flight Time, Keystroke Dynamics, Neural Network, Up-Up Time, User Authentication.
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