Smartphone continuous authentication based on keystroke and gesture profiling

Recently, smartphones have become increasingly popular, whereas data leakage continues to be a serious problem for many large organizations. Consequently, smartphone applications containing sensitive personal or company data are at risk when targeted by attackers. Continuous and passive authentication is a popular scheme for secretly classifying users´ identities based on their own unique touch motions (i.e., keystrokes and gestures). However, previous methods are inadequate when classifying users´ singular touch motions. In this paper, we propose a novel continuous authentication method. The proposed method not only profiles behavioral biometrics from keystrokes and gestures, it also acquires the specific properties of a one-touch motion during the user´s interaction with the smartphone. We demonstrate that the manner by which a user uses the touchscreen - that is, the specific location touched on the screen, the drift from when a finger moves up and down, the area touched, and the pressure used - reflects unique physical and behavioral biometrics. Moreover, the speed of the Gesture Segment (GS) is defined to extract a meaningful velocity segment. Experiments conducted to evaluate the proposed method for combining keystroke and gesture behavior demonstrate its effectiveness and accuracy.