Curbing mobile malware based on user-transparent hand movements

In this paper, we present a run-time defense to the malware that inspects the presence/absence of certain transparent human gestures exhibited naturally by users prior to accessing a desired resource. Specifically, we focus on the use of transparent gestures to prevent the misuse of three critical smartphone capabilities - the phone calling service, the camera resource and the NFC reading functionality. We show how the underlying natural hand movement gestures associated with the three services, calling, snapping and tapping, can be detected in a robust manner using multiple - motion, position and ambient - sensors and machine learning classifiers. To demonstrate the effectiveness of our approach, we collect data from multiple phone models and multiple users in real-life or near real-life scenarios emulating both benign settings as well as adversarial scenarios. Our results show that the three gestures can be detected with a high overall accuracy, and can be distinguished from one another and from other activities (benign or malicious), serving as a viable malware defense. In the future, we believe that transparent gestures associated with other smartphone services, such as sending SMS or email, can also be integrated with our system.