KDPrint: Passive Authentication using Keystroke Dynamics-to-Image Encoding via Standardization

TL;DR

This paper introduces KDPrint, a passive user authentication method using keystroke dynamics encoded as images, which significantly improves accuracy over existing techniques for mobile PIN-based authentication.

Contribution

The paper proposes a novel image encoding technique for keystroke data that enhances the performance of passive authentication systems on mobile devices.

Findings

Achieved an EER of 6.7%, outperforming previous methods with 47.7%.

Attained a TAR of 94.4% and FAR of 8% for 17 users.

Surpassed existing methods in information capacity with the proposed imaging approach.

Abstract

In contemporary mobile user authentication systems, verifying user legitimacy has become paramount due to the widespread use of smartphones. Although fingerprint and facial recognition are widely used for mobile authentication, PIN-based authentication is still employed as a fallback option if biometric authentication fails after multiple attempts. Consequently, the system remains susceptible to attacks targeting the PIN when biometric methods are unsuccessful. In response to these concerns, two-factor authentication has been proposed, albeit with the caveat of increased user effort. To address these challenges, this paper proposes a passive authentication system that utilizes keystroke data, a byproduct of primary authentication methods, for background user authentication. Additionally, we introduce a novel image encoding technique to capture the temporal dynamics of keystroke data, overcoming the performance limitations of deep learning models. Furthermore, we present a methodology for selecting suitable behavioral biometric features for image representation. The resulting images, depicting the user's PIN input patterns, enhance the model's ability to uniquely identify users through the secondary channel with high accuracy. Experimental results demonstrate that the proposed imaging approach surpasses existing methods in terms of information capacity. In self-collected dataset experiments, incorporating features from prior research, our method achieved an Equal Error Rate (EER) of 6.7%, outperforming the existing method's 47.7%. Moreover, our imaging technique attained a True Acceptance Rate (TAR) of 94.4% and a False Acceptance Rate (FAR) of 8% for 17 users.