Related works for applying keystroke dynamics (KD) on free text identification indicated that applying KD can improve the accuracy of personal authentication on free text. As the result, this paper proposes a new biometrics, i.e., the keystroke clusters map (KC-Map), by clustering users’ keystrokes in order to effectively enhance the accuracy of personal authentication in free text. Since KC-Map is conducted via clustering, it is not suitable for traditional classifiers. In order to tackle this problem, the paper further proposes a keystroke clusters map similarity classifier (KCMS classifier). Experimental results positively show that the proposed KC-Map and KCMS classifier can efficiently improve the accuracy of personal authentication on free text with up to 1.27 times. In addition, one of the huge disadvantages on the current approaches in free text identification is that users are generally required to be trained for several months. Longer training time makes free text identification more impractical. Another motivation of this paper is to explore whether it is possible to shorten the training time into an acceptable range. Experimental results show that, to achieve relatively fair identification accuracy, users only need to carry out about 20 min for training.
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