A Survey of Keystroke Dynamics Biometrics <s> Growing Window. <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> Growing Window. <s> This paper describes a novel technique to strengthen password authentication system by incorporating multiple keystroke dynamic information under a fusion framework. We capitalize four types of latency as keystroke feature and two methods to calculate the similarity scores between the two given latency. A two layer fusion approach is proposed to enhance the overall performance of the system to achieve near 1.401% Equal Error Rate (EER). We also introduce two additional modules to increase the flexibility of the proposed system. These modules aim to accommodate exceptional cases, for instance, when a legitimate user is unable to provide his or her normal typing pattern due to reasons such as hand injury. <s> BIB002 </s> A Survey of Keystroke Dynamics Biometrics <s> Growing Window. <s> Keystroke dynamics is a behavioral biometrics showing a degradation of performance when used over time. This is due to the fact that the user improves his/her way of typing while using the system, therefore the test samples may be different from the initial template computed at an earlier stage. One way to bypass this problem is to use template update mechanisms. We propose in this work, new semi-supervised update mechanisms, inspired from known supervised ones. These schemes rely on the choice of two thresholds (an acceptance threshold and an update threshold) which are fixed manually depending on the performance of the system and the level of tolerance in possible inclusion of impostor data in the update template. We also propose a new evaluation scheme for update mechanisms, taking into account performance evolution over several time-sessions. Our results show an improvement of 50% in the supervised scheme and of 45% in the semi-supervised one with a configuration of the parameters chosen so that we do not accept many erroneous data. <s> BIB003
This method was alternatively known as progressive mode by BIB001 . The idea behind this technique is to append the latest user sample to their existing reference template. By doing so, the size of reference template may be increasing indefinitely, which may cause storage overhead. However, some algorithms employed may be spared or adjusted to avoid this consequence. For example, BIB002 utilized an alternative version of mean and standard deviation to avoid storing the entire preceding keystroke timing values. Nevertheless, the implementation of growing window is better than no adaptation at all BIB003 .