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Keystroke dynamics recognition based on personal data: A comparative experimental evaluation implementing reproducible research

This work proposes a new benchmark for keystroke dynamics recognition on the basis of fully reproducible research. Instead of traditional authentication approaches based on complex passwords, we propose a novel keystroke recognition based on typing patterns from personal data. We present a new database made up with the keystroke patterns of 63 users and 7560 samples. The proposed approach eliminates the necessity to memorize complex passwords (something that we know) by replacing them by personal data (something that we are). The results encourage to further explore this new application scenario and the availability of data and source code represent a new valuable resource for the research community.