Biblio

Traditional authentication techniques such as static passwords are vulnerable to replay and guessing attacks. Recently, many studies have been conducted on keystroke dynamics as a promising behavioral biometrics for strengthening user authentication, however, current keystroke based solutions suffer from a numerous number of features with an insufficient number of samples which lead to a high verification error rate and high verification time. In this paper, a keystroke dynamics based authentication system is proposed for cloud environments that supports fixed and free text samples. The proposed system utilizes the ReliefF dimensionality reduction method, as a preprocessing step, to minimize the feature space dimensionality. The proposed system applies clustering to users' profile templates to reduce the verification time. The proposed system is applied to two different benchmark datasets. Experimental results prove the effectiveness and efficiency of the proposed system.