Implicit Continuous Authentication Model Based on Mobile Terminal Touch Behavior

Most existing identity authentication technologies rely on some ways for the first login authentication, such as personal identification number (PIN), track, or biological characteristics. However, these ways exist plenty of security risks, which make people face password guessing attacks, trace attacks, and shoulder surfing attacks for a long time. Once the illegal users forge identity to complete authentication or bypass first login authentication, their subsequent behavior will become out of control. To solve the above problems, we propose an implicit continuous authentication model based on the touch behavior of the mobile terminal. The model uses the data collected by the accelerometer, gyroscope, and magnetometer to generate feature vectors and extracts the feature vectors containing macroscopic features, microscopic features, and joint features. And we design a convolutional bidirectional recurrent neural network model to distinguish the sensor feature vectors. On this basis, we perform various experiments on a large dataset Hand Movement, Orientation, and Grasp (HMOG) with different sensor characteristics. Compared with the most advanced models proposed recently, the results show that our model achieves an equal error rate (EER) of 0.53%, which significantly improves authentication accuracy.