  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.
