Adaptive Mobile Keystroke Dynamic Authentication Using Ensemble Classification Methods

摘要

Mobile keystroke dynamic biometric authentication requires several biometric samples for enrolment. In some application context or scenario where the user scarcely uses the application, it could take quite a while to get enough samples for enrolment. This creates a window of vulnerability where the user cannot be authenticated using the keystroke dynamic biometric. We propose in this paper, an adaptive approach to derive initially the user profile online and passively with a minimum number of samples, and then progressively update the profile as more samples become available. The approach uses ensemble classification methods and the equal error rate as profile maturity metric. The approach was evaluated using an existing dataset involving 42 users yielding encouraging results. The best performance achieved was an EER of 5.29% using Random forest algorithm.