Can't you hear me knocking: Identification of user actions on Android apps via traffic analysis
This addresses privacy concerns for smartphone users by demonstrating how passive adversaries can infer detailed activities without device control, though it is incremental as it builds on prior traffic analysis work.
The paper tackles the problem of identifying specific user actions on Android apps by analyzing encrypted network traffic, achieving over 95% accuracy and precision for most actions like social media interactions and email sending.
While smartphone usage become more and more pervasive, people start also asking to which extent such devices can be maliciously exploited as "tracking devices". The concern is not only related to an adversary taking physical or remote control of the device (e.g., via a malicious app), but also to what a passive adversary (without the above capabilities) can observe from the device communications. Work in this latter direction aimed, for example, at inferring the apps a user has installed on his device, or identifying the presence of a specific user within a network. In this paper, we move a step forward: we investigate to which extent it is feasible to identify the specific actions that a user is doing on his mobile device, by simply eavesdropping the device's network traffic. In particular, we aim at identifying actions like browsing someone's profile on a social network, posting a message on a friend's wall, or sending an email. We design a system that achieves this goal starting from encrypted TCP/IP packets: it works through identification of network flows and application of machine learning techniques. We did a complete implementation of this system and run a thorough set of experiments, which show that it can achieve accuracy and precision higher than 95%, for most of the considered actions.