Shared research dataset to support development of keystroke authentication

Keystroke authentication can help significantly improve computer security by hardening passwords or offering active, continuous authentication. Over the years, many keystroke authentication algorithms have been reported to produce promising results. However, these results are tested on proprietary datasets with varying numbers of subjects and amounts of text, making it difficult to compare and improve the state of art. We describe a new dataset that we have developed with the goal to serve as a shared common testbed to enable future improvements. The new dataset includes keystroke data for short pass-phrases, fixed text (transcription of long proses), and free text. It also includes video of a subject´s facial expression and hand movement during the data collection sessions, allowing for a deeper understanding of why an algorithm works the way it does, for example, by finding out whether a subject is a touchtypist or not. As a baseline for benchmarking, we also include the results of replicating two existing algorithms using the new dataset.