Keystroke Dynamics Based User Authentication and its Application in Online Examination

Abstract

Currently user authentication and identity monitoring are required in various collaborative computer supported systems, such as online assessments and examinations. However, existing authentication methods such as passwords checking are less reliable. In addition, identity monitoring is hard to be realized effectively and efficiently for applications based on collaborative architecture. In this paper, we leverage keystroke dynamics to explore biometrics security and propose a user authentication framework based on edge computing architecture to address these issues. To support both static and continuous authentications with high accuracy and efficiency, dynamically improved keystroke profiles, Gaussian model based anomaly detector and keystroke stream processing are designed in the framework. The feasibility and effectiveness of the framework are verified by three representative public data sets and a real-world case study. The results show that the authentication can proceeds efficiently to enable an undisturbed and secure environment for online examinations.