A Behavioral Biometric Authentication Framework on Smartphones

To protect smartphones from unauthorized access, the user has the option to activate authentication mechanisms : PIN, Password, or Pattern. Unfortunately, these mechanisms are vulnerable to shoulder-surfing, smudge and snooping attacks. Even the traditional biometric based systems such as fingerprint or face, also could be bypassed. In order to protect smartphones data against these sort of attacks, we propose a behavioral biometric authentication framework that leverages the user's behavioral patterns such as touchscreen actions, keystroke, application used and sensor data to authenticate smartphone users. To evaluate the framework, we conducted a field study in which we instrumented the Android OS and collected data from 52 participants during 30-day period. We present the prototype of our framework and we are working on its components to select the best features set that can be used to build different modalities to authenticate users on different contexts. To this end, we developed only one modality, a gesture authentication modality, which authenticate smartphone users based on touch gesture. We evaluated this authentication modality on about 3 million gesture samples based on two schemes, classification scheme with EER~0.004, and anomaly detection scheme with EER~0.10.