Enhancing smartphone security with human centric bimodal fallback authentication leveraging sensors

Smartphones store valuable personal information, necessitating robust authentication methods to protect user data. This research proposes a lightweight bi-model fallback authentication technique that combines dynamic security questions and finger pattern recognition using inertial measurement units. The dynamic security questions are generated based on the smartphone's usage behavior, while the owner's finger movements are captured using four different inertial sensors: accelerometer, gyroscope, gravity sensor, and magnetometer. By combining the answers to the questions and the owner's finger movements, the user can be authenticated even if the primary authentication method fails. In this study, data was collected from 24 participants, including 12 primary phone users and 12 close adversaries, over a span of 28 days. The dynamic security questions, derived from call, SMS, battery charging events, application usage, location, and physical activity categories, achieved high accuracy rates, with call, SMS, and application usage surpassing $$ . Incorporating the inertial measurement units significantly improved the accuracy of all question types, increasing from a maximum of $$ to $$ , while also enhancing the True Positive Rate from 0.79 to 0.99 compared to a previous study. This research presents a promising lightweight bi-model fallback authentication technique that leverages dynamic security questions and inertial measurement units data, demonstrating its effectiveness for enhancing smartphone security.