Sensor Fusion Based Implicit Authentication for Smartphones

Implicit authentication, as a novel identity authentication mechanism, has received widespread attention. However, the performance of implicit authentication still needs to be improved. In this paper, we propose a sensor fusion based implicit authentication system to enhance the protection level of the identity authentication mechanism for smartphone. First, the sensor used to characterize user behavior is determined by analyzing the authentication performance of each sensor. Then, considering the practicability of the system, one-class classification algorithm One-Class Support Vector Machine (OC-SVM) is used to train the authentication model. Finally, the decision function of each sensor is weighted and fused to deliver a result. Based on the actual data set of 7,500 samples collected from 75 participants, the effectiveness of the system is verified in different operating environments and varied passwords. The results show that the proposed system can improve the accuracy of identity authentication effectively.