KeyRecs: Keystroke Dynamics Dataset

<p>KeyRecs is a&nbsp;keystroke dynamics dataset that can be used to train, validate, and test machine learning models for anomaly detection and robust typing pattern recognition, as well as&nbsp;the clustering and classification of users that present a similar behavior. It contains&nbsp;fixed-text and free-text samples of&nbsp;user typing behavior, obtained in a study with 100 participants of 20 different nationalities performing password retype and transcription exercises.</p> <p>The samples consist of inter-key latencies computed by measuring the time between each key press and release during an exercise, following a digraph model. Additionally, the participants were also asked to provide their demographic information regarding age, gender, handedness, and nationality.</p> <p>KeyRecs can be valuable to&nbsp;enhance the recognition of authorized users and prevent illegal logins&nbsp;in biometric authentication software, and can be&nbsp;combined with additional data recordings to create more extensive datasets and improve the generalization of machine learning models.</p> <p>If you use this dataset, please cite the primary data article: <a href="https://doi.org/10.1016/j.dib.2023.109509">https://doi.org/10.1016/j.dib.2023.109509</a></p> <p>&nbsp;</p>