A continuous identity verification method based on free-text keystroke dynamics

Currently, existing continuous identity verification methods mostly need to analyze a lot of keystroke data to ensure the authentication credibility. To achieve certification results with less data, in the paper, a new continuous verification method is proposed. This method, based on free-text keystroke dynamics, excavates the nearest character sequences of the users from their typing patterns, then builds Gaussian model based on the users nearest character sequences, and at last grades the attempts to verify identity of the users based on Gaussian probability density function. Experimental results demonstrate that efficacy of the proposed method with accuracy of 90.5%, which achieve a false-alarm-rate of 5.3% under thirty characters. In the field of continuous identity verification, our method can be applied to reduce the verification cycle and ensure the reliability of the verification.