KDPrint: Passive authentication using keystroke dynamics-to-image encoding via standardization

Abstract

Personal identification number (PIN) authentication remains prevalent in mobile and IoT systems due to its simplicity, yet it is inherently vulnerable to various attacks such as shoulder surfing, smudge analysis, and brute force attempts. To reinforce its security without compromising usability, we propose KDPrint, a passive authentication framework that transforms keystroke dynamics into graph-based image representations. By applying a hash-based permutation and standardized feature processing, KDPrint captures the temporal and spatial structure of user behavior while mitigating raw data exposure. The resulting images are used with lightweight anomaly detection models, enabling accurate user verification under resource-constrained environments. Experiments involving 50 participants across both laboratory and real-world environments demonstrated that KDPrint maintained robustness under two adversarial scenarios: an EER of 3.3 % when only the PIN was leaked, and an EER of 4.4 % when both the PIN and behavioral characteristics were exposed. These results demonstrate that KDPrint offers a practical and interpretable solution for augmenting PIN authentication in mobile and IoT systems, balancing robustness, efficiency, and user transparency.