A Survey of Keystroke Dynamics Biometrics <s> Sample Collection Interval. <s> We present in this paper a study on the ability and the benefits of using a keystroke dynamics authentication method for collaborative systems. Authentication is a challenging issue in order to guarantee the security of use of collaborative systems during the access control step. Many solutions exist in the state of the art such as the use of one time passwords or smart-cards We focus in this paper on biometric based solutions that do not necessitate any additional sensor. Keystroke dynamics is an interesting solution as it uses only the keyboard and is invisible for users. Many methods have been published in this field. We make a comparative study of many of them considering the operational constraints of use for collaborative systems. <s> BIB001 </s> A Survey of Keystroke Dynamics Biometrics <s> Sample Collection Interval. <s> Among all the existing biometric modalities, authentication systems based on keystroke dynamics present interesting advantages. These solutions are well accepted by users and cheap as no additional sensor is required for authenticating the user before accessing to an application. In the last thirty years, many researchers have proposed, different algorithms aimed at increasing the performance of this approach. Their main drawback lies on the large number of data required for the enrollment step. As a consequence, the verification system is barely usable, because the enrollment is too restrictive. In this work, we propose a new method based on the Support Vector Machine (SVM) learning satisfying industrial conditions (i.e., few samples per user are needed during the enrollment phase to create its template). In this method, users are authenticated through the keystroke dynamics of a shared secret (chosen by the system administrator). We use the GREYC keystroke database that is composed of a large number of users (100) for validation purposes. We compared the proposed method with six methods from the literature (selected based on their ability to work with few enrollment samples). Experimental results show that, even though the computation time to build the template can be longer with our method (54 s against 3 s for most of the others), its performance outperforms the other methods in an industrial context (Equal Error Rate of 15.28% against 16.79% and 17.02% for the two best methods of the state-of-the-art, on our dataset and five samples to create the template, with a better computation time than the second best method). <s> BIB002
As discussed in the previous section, the greater number of samples collected the more accurate and conclusive a test result can be from statistical point of view. However, it is impractical to request huge amount of inputs from user at a single instance. More importantly keystroke dynamics are to behavioral biometrics where variability of typing is expected appear across different sittings  . Therefore, several sessions of data collection would ideally leverage one's typing evolution. In view of this, some researchers split the data collection phase into several different frequencies and interval separation length. These include a daily sitting over three weeks duration  , three sessions within six days BIB001 , or five sessions with one week apart BIB002 . Having said that, the majority data collected in keystroke dynamics literature were within one sitting (73%). Problems such as user availability and commitment for corresponding sessions might be a pullback factor for employing multiple session data collection.