.png){kind=icon}
Return
Continuous User Identification Across Devices Using Keystroke Dynamics
.png){kind=icon}
PRE
AI
DOI：10.1007/978-3-032-10486-1_6.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Keystroke dynamics is a well-established behavioral biometric for continuous user identification. However, most existing models are device-specific, leading to performance degradation when users switch between desktop keyboards and touch-based devices. This presents a challenge in today's multi-device environments, where seamless and secure identification is essential. In this work, we propose a unified Transformer-based model for cross-device user identification using keystroke dynamics. Leveraging transfer learning, our approach learns a shared representation of typing patterns from both desktop and mobile inputs, enabling robust identification across heterogeneous devices without requiring separate models. Experiments on the Keystroke 136M and Aalto Mobile datasets show that our model achieves Equal Error Rates (EERs) of 2.45 on desktop, 1.76 on mobile, and 2.63 in cross-device scenarios. These results confirm the effectiveness of our approach in achieving reliable, device-agnostic identification while improving usability and security in real-world, multi-platform settings.
Keywords:
Keystrokes
Cross-devices
Mobile
Desktop
Biometrics
Transformer
Cybersecurity
Journal
I
IF:
0
Papers:
50
Citations:
0
Organization
Cited Papers
ShareSaveDouble serial adaptation mechanism for keystroke dynamics authentication based on a single password.png)
COMPUTERS & SECURITY
OAAI
COMPUTERS & SECURITYIF5.4
OAAIShareSaveShareSaveStatistical median-based classifier model for keystroke dynamics on mobile devices2016 Sixth International Conference on Digital Information Processing and Communications (ICDIPC)
2016 Sixth International Conference on Digital Information Processing and Communications (ICDIPC)IF0
PREAI
ShareSaveShareSaveFreely typed keystroke dynamics-based user authentication for mobile devices based on heterogeneous featuresPATTERN RECOGNITION
PATTERN RECOGNITIONIF7.6
PREAI
ShareSave
View more