https://woas-journals.com/index.php/ijitas <p><strong>International Journal of Information Technology and Applied Sciences (IJITAS) -ISSN 2709-2208 (Online)-</strong> is a peer-reviewed International Journal that currently publishes 4 issues annually. IJITAS is published by the <a href="http://www.woasjournals.com/" target="_blank" rel="noopener">World Organization of Applied Sciences (WOAS)</a>. IJITAS journal publishes technical papers, as well as review articles and surveys, describing recent research and development work that covers all areas of computer science, information systems, and computer / electrical engineering.</p> <p align="justify"><em><strong>Cross Reference</strong></em></p> <p align="justify"><strong>International Journal of Information Technology and Applied Sciences (IJITAS)</strong> is a member of the <strong>CrossRef. </strong>The DOI prefix allotted for IJITAS is <a href="https://doi.org/10.52502/ijitas"><strong>10.52502/ijitas</strong></a></p> International Journal of Information Technology and Applied Sciences (IJITAS) en-US International Journal of Information Technology and Applied Sciences (IJITAS) 2709-2208 https://woas-journals.com/index.php/ijitas/article/view/1279 <p>Keystroke dynamics has been studied for decades as a behavioral biometric, but the vast majority of this work has been directed toward user authentication. This study instead investigates an underexplored application: estimating user age from keystroke patterns in order to identify underage users, offering a lightweight, privacy-preserving complement to existing age-verification methods. Using free-text keystroke data collected from 80 volunteers aged 16 to 60, we formulate age estimation as a binary classification problem distinguishing users under 21 from adults, and evaluate three classifiers, K-Nearest Neighbor, Support Vector Machine, and Random Forest, on a comprehensive set of timing-derived features. Our best-performing model achieves a recall of 0.86, with the two strongest classifiers averaging an F1-score of 0.71, indicating that the incidental behavioral signal present in ordinary typing carries meaningful information about a user's age. These findings point to keystroke dynamics as a promising tool for addressing the growing challenge of child safety online.<br>Keywords: Keystroke Dynamics; Age Verification; Machine Learning; Behavioral Biometrics; Child Online Safety.</p> Sarker Tanveer Ahmed Rumee Ayon Shahrier Md. Abid Raihan Farkhunda Dorin Copyright (c) 2026 https://creativecommons.org/licenses/by-nc-nd/4.0 2026-09-29 2026-09-29 8 3 95 103 10.5281/zenodo.23048621