Enhanced User Authentication through Trajectory Clustering

TL;DR

This paper proposes an economic biometric authentication method based on user typing rhythm patterns, using trajectory clustering of key event latencies to improve security over traditional passwords.

Contribution

It introduces a novel approach of trajectory clustering on key event latencies for user authentication, emphasizing cost-effectiveness and behavioral biometrics.

Findings

Achieved distinguishable user patterns through trajectory clustering.

Validated method on data from 100 users showing promising accuracy.

Enhanced security by analyzing typing rhythm as a behavioral biometric.

Abstract

Password authentication is the most commonly used technique to authenticate the user validity. However, due to its simplicity, it is vulnerable to pseudo attacks. It can be enhanced using various biometric techniques such as thumb impression, finger movement, eye movement etc. In this paper, we concentrate on the most economic technique, based on the user habitual rhythm pattern i.e. not what they type but how they type is the measure for authenticating the user. We consider the latency between key events as the trajectory, and trajectory clustering is used to obtain the hidden patterns of the user. Obtained pattern can be considered as a cluster of measurements that can be used to differentiate from other users. We evaluated the proposed technique on the data obtained from the 100 users.