A Survey of Keystroke Dynamics Biometrics <s> Effectiveness. <s> Keystroke dynamics is a behavioral biometric that is based on how a user enters their login details. In this study, a set of eight attributes were extracted during the course of entering login details. This collection of attributes was used to form a reference signature (a biometrics identification record) for subsequent authentication requests. The algorithm for the authentication step entails transforming the attributes into a discretised form based on the amino acid alphabet. A set of bioinformatics based algorithms are then used to perform the actual authentication test. In addition, the use of rough sets was employed in this study to determine if subsets of attributes were more important in the classification (authentication) than others. Lastly, the results of this study indicate that the error rate is less than 1% in the majority of the cases. <s> BIB001 </s> A Survey of Keystroke Dynamics Biometrics <s> Effectiveness. <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> BIB002
Effectiveness indicates the ability of a method to correctly differentiate genuine and imposter. Performance indicators employed by the researches are summarized as follow. False Rejection Rate (FRR) refers to the percentage ratio between falsely denied genuine users against the total number of genuine users accessing the system. Occasionally known as False Nonmatch Rate (FNMR) BIB002 or type 1 error  . A lower FRR implies less rejection and easier access by genuine user. False Acceptance Rate (FAR) is defined as the percentage ratio between falsely accepted unauthorized users against the total number of imposters accessing the system. Terms such as False Match Rate (FMR) BIB002 or type 2 error  refers to the same meaning. A smaller FAR indicates less imposter accepted. Equal Error Rate (EER) is used to determine the overall accuracy as well as a comparative measurement against other systems. It may be sometimes referred to as Crossover Error Rate (CER) BIB001 . Result comparison portrayed in the next section will mainly be express with FAR, FRR, and EER.