A Survey of Keystroke Dynamics Biometrics <s> Public Data Set. <s> Even if the market penetration rate of biometric technologies is still far below its potential, many biometric systems are used in our daily real-life. One of the main reasons to its low proliferation is the lack of a generic and complete approach that quantifies the performance of biometric systems taking into account individuals' perception among the process. Among all the existing biometric modalities, authentication systems based on keystroke dynamics are particularly interesting. Many researchers proposed in the last decades some algorithms to increase the efficiency of this approach. Nevertheless, none significant benchmark is available and commonly used in the state of the art to compare them by using a similar and rigorous protocol. We propose in this paper: a benchmark testing suite composed of a database and a software that are available for the scientific community for the evaluation of keystroke dynamics based systems. Performance evaluation of various keystroke dynamics methods tested on the database is available in [1]. <s> BIB001 </s> A Survey of Keystroke Dynamics Biometrics <s> Public Data Set. <s> Keystroke dynamics-the analysis of typing rhythms to discriminate among users-has been proposed for detecting impostors (i.e., both insiders and external attackers). Since many anomaly-detection algorithms have been proposed for this task, it is natural to ask which are the top performers (e.g., to identify promising research directions). Unfortunately, we cannot conduct a sound comparison of detectors using the results in the literature because evaluation conditions are inconsistent across studies. Our objective is to collect a keystroke-dynamics data set, to develop a repeatable evaluation procedure, and to measure the performance of a range of detectors so that the results can be compared soundly. We collected data from 51 subjects typing 400 passwords each, and we implemented and evaluated 14 detectors from the keystroke-dynamics and pattern-recognition literature. The three top-performing detectors achieve equal-error rates between 9.6% and 10.2%. The results-along with the shared data and evaluation methodology-constitute a benchmark for comparing detectors and measuring progress. <s> BIB002
To the best of our knowledge, we are able to access three publically available data sets shared online BIB001  BIB002 . Although they may not be comparable to benchmark data set of other biometrics modalities, however, full credit should be given on the attempt to share their resource with the community. Since data collection is not a straightforward task, by doing so, at least, entry level researcher may have a platform to work on. A simple comparison among the data set can be seen in Table 3 .