Keystroke Mobile Authentication: Performance of Long-Term Approaches and Fusion with Behavioral Profiling

In this paper we evaluate the performance of mobile keystroke authentication according to: (1) data availability to model the user; and (2) combination with behavioral-based profiling techniques. We have developed an ensemble of three behavioral based-profile authentication techniques (WiFi, GPS Location, and App usage) and a Keystroke state-of-the-art recognition approach. Algorithms based on template update are employed for profiling systems meanwhile bidirectional recurrent neuronal networks with a Siamese training setup is used for the keystroke system. Our experiments are conducted on the semi-uncontrolled UMDAA-02 database. This database comprises smartphone sensor signals acquired during natural human-mobile interaction. Our results show that it is necessary 6 days of usage data stored to achieve the best performance in average. The template update allows to improve the equal error rate of keystroke by a relative 20%-30% performance.