Implicit Identity Authentication Method Based on User Posture Perception

Smart terminals use passwords and physiological characteristics such as fingerprints to authenticate users. Traditional authentication methods work when users unlock their phones, but they cannot continuously verify the user’s legal identity. Therefore, the one-time authentication implemented by conventional authentication methods cannot meet security requirements. Implicit authentication technology based on user behavior characteristics is proposed to achieve the continuous and uninterrupted authentication of savvy terminal users. This paper proposes an implicit authentication method that fuses keystroke and sensor data. To improve the accuracy of authentication, a neural network-based feature extraction model that integrates keystroke data and motion sensor data is designed. A feature space with dual-channel fusion is constructed, and a dataset collected in real scenarios is built by considering the changes in user activity scenarios and the differences in terminal holding postures. Experimental results on the collected data show that the proposed method has improved the accuracy of user authentication to a certain extent.