Implicit Identity Authentication Mechanism based on Smartphone Touch Dynamics

Information security has now become an important area of concern. Considering the shortcomings in traditional unlocking ways of smartphones, this paper proposes an implicit identity authentication mechanism by extracting users' touch dynamic characteristics. 86-dimensional features from two types of sensor data (e.g., acceleration and gyroscope) generated when a user unlocks a smartphone are extracted to characterize the user's behavior. Particularly, we adopt three popular classifiers: support vector machine (SVM), K-nearest neighbor (KNN) and random forest (RF) to perform training. Finally, we verify the accuracy of the classifier. Experimental results show that the RF classifier achieves an ideal accuracy rate for passwords with different levels of repetition, and the average accuracy rate is over 98%.