A review on the public benchmark databases for static keystroke dynamics <s> Keystroke100 <s> This paper presents the development of a keystroke dynamics-based user authentication system using the ARTMAP-FD neural network. The effectiveness of ARTMAP- FD in classifying keystroke patterns is analyzed and compared against a number of widely used machine learning systems. The results show that ARTMAP-FD performs well against many of its counterparts in keystroke patterns classification. Apart from that, instead of using the conventional typing timing characteristics, the applicability of typing pressure to ascertaining user's identity is investigated. The experimental results show that combining both latency and pressure patterns can improve the equal error rate (ERR) of the system. <s> BIB001 </s> A review on the public benchmark databases for static keystroke dynamics <s> Keystroke100 <s> Among all the existing biometric modalities, authentication systems based on keystroke dynamics are particularly interesting for usability reasons. Many researchers proposed in the last decades some algorithms to increase the efficiency of this biometric modality. Propose in this paper: a benchmark testing suite composed of a database containing multiple data (keystroke dynamics templates, soft biometric traits ...), which will be made available for the research community and a software that is already available for the scientific community for the evaluation of keystroke dynamics based systems. We also built the proposed biometric database on soft biometric traits for keystroke dynamics to suit the experiment. 110 people had voluntarily participated and gave their soft biometrics data i.e. the way of typing gender age and handedness. <s> BIB002
Loy et al. have created a public KD dataset, which also contains pressure information BIB001 . 100 users provided 10 samples each. In our experiments, we only used the latency information, not the pressure one. The dataset 9 is available in text files. BIB002 . However it can also be used for classical keystroke dynamics authentication. 110 users provided 10 inputs of 5 different passwords during several sessions typed with one hand and 10 additional ones with two hands. Acquisition has been done with the same software than the GREYC database. The dataset 10 is available in Excel file.