Predicting user age by keystroke dynamics

Abstract

Keystroke dynamics is investigated over 30 years because of its biometric properties, but most of the studies are focusing on identification. In current study our goal is to predict user age by keystroke data. We collected keystroke data through different real life online systems during 2011 and 2018. Data logs were labeled with user age, gender and in some cases with other available information. We analyzed 2.3 million keystrokes, from 7119 keystroke data logs, produced by ca 1000 individual subjects, presenting six different age groups. All these data logs are also made available to research community, and the web address is provided in the paper. We carried out binary and multiclass classification using supervised machine-learning methods. Binary classification results were all over the baseline, best f-score over 0.92 and lowest 0.82. Multiclass classification distinguished all groups over baseline. Analyzing distinguishing features, we found overlap with text-mining features from previous studies.