Since few decades, the simple password authentication has either replaced or compounded with biometrics (such as Facial Recognition, Fingerprint Scan etc.) to provide better security. Keystroke Dynamics is behavioral biometrics that can perform continuous authentication to detect intruders. In this paper, we investigate whether user specific password gives better performance than artificially rhythmed password. Also, impact of sensory data on overall performance of the system is examined. Finally, Genetic Algorithm is used to optimize the features. The features used to analyze the user data were hold time, flight time and X, Y and Z axis reading from accelerometer sensor. Results showed that user data gives better performance than artificially rhythmed passwords. Best accuracy of around 90% was achieved by using user specified passwords and optimizing the results with genetic algorithm.

Keystroke dynamics, Typing behaviour, Mobile, Authentication, Biometrics

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