Machine learning-based novel continuous authentication system using soft keyboard typing behavior and motion sensor data

Abstract

Smartphones utilize various authentication methods, including passwords, fingerprints, and face recognition. While this information is quite practical and easy to remember, it introduces several security issues. The primary concerns involve theft, password forgetfulness, or unauthorized password copying. Implementing behavioral biometrics for user authentication adds an extra layer of security. The main contribution of this study is the utilization of soft keyboard typing behavior, a behavioral biometric, for continuous user recognition. To achieve this, the phone's grip style and typing characteristics of users are scrutinized using data collected from motion sensors and the touchscreen panel. Another challenge in mobile device authentication pertains to recognition accuracy and processing time. To expedite and optimize data classification, a hybrid classification structure is suggested. This structure incorporates correlation-based feature selection and a straightforward logistic regression method, offering rapid and highly accurate classification outcomes—a further contribution of this study. Experimental results demonstrate that user identification can be accomplished in as little as 0.03 ms, with a classification accuracy of up to 93%. Continuous authentication systems offer greater security compared to one-time authentication systems. Nevertheless, these systems might not always yield the most precise results. Overcoming this challenge necessitates the development of an efficient software architecture. In line with this, an additional contribution of this study is an explanation of how to construct a continuous authentication system using the developed architecture.