An Implicit Authentication Solution based on User's Keystroke Behavior of Smartphone Usage
Dawei Dai, Weitian Chen, Shengteng Jiang, Renzhong Wang, Dan Tao · 2019
With the popularity of smartphones, more and more users are accustomed to storing sensitive information related to personal privacy, which puts forward higher requirements for the current authentication system. In this paper, an implicit authentication solution based on user's keystroke behavior of smartphone usage is proposed. Firstly, a user's behavior data are collected by three types of built-in sensors in a smartphone. After performing smoothing and normalization processing, we extract temporal features in time domain by using statistical methods. Secondly, various classifiers are used to classify whether a user reflected by features are legal or not. Finally, we do simulations to verify the classification performances of these classifiers, and draw a conclusion that the classification performances of GentleBoost and LogitBoost classifiers are superior to those of others with our experimental dataset.