A Mobile App Authentication Approach by Fusing the Scores from Multi-Modal Data

Mobile phones have become an essential part of our daily life. We are heavily relying on various applications (App) installed on our mobile phones to socialize and even do business. The majority of mobile applications require some forms of authentication measurement often combining usernames and passwords, however, memorizing various passwords for different applications is a nightmare for the users. In order to relieve people from memorizing these passwords, an unobtrusive mobile application authentication approach is designed in this paper by analyzing the data collected from four resources: WiFi, Bluetooth, accelerometer sensor and gyroscope sensor. We first develop the authentication model based on single resource respectively. Furthermore, a score-level fusion authentication approach is proposed by considering the scores generated from four models. The proposed approach was evaluated on a dataset collected from a real-life scenario with the authentication response time setting at 3 seconds. The lowest EER (Equal Error Rate) achieved from the experiments is 9.67%, which indicates the feasibility of deploying the proposed approach on mobile phone to enhance the security while still maintaining good user-fripnillinpss_