We design a system that achieves this goal by using advanced machine learning techniques. 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 mobile apps, by eavesdropping their encrypted network traffic.
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. The concern is not only related to an adversary taking physical or remote control of the device, but also to what a passive adversary without the above capabilities can observe from the device communications. While smartphone usage become more and more pervasive, people start also asking to which extent such devices can be maliciously exploited as "tracking devices". Our evaluation with 35 widely popular app activities (ranging from social networking and dating to personal health and presidential campaigns) shows that NetScope yields high detection accuracy (78.04% precision and 76.04% recall on average).
By learning the subtle traffic behavioral differences between activities (e.g., “browsing” versus “chatting” in a dating app), NetScope is able to perform robust inference of users’ activities, for both Android and iOS devices, based solely on inspecting IP headers.
Despite the widespread use of fully encrypted communication, our technique, called NetScope, is based on the intuition that the highly specific implementation of each app leaves a fingerprint on its traffic behavior (e.g., transfer rates, packet exchanges, and data movement). In this paper we will demonstrate that a passive eavesdropper is capable of identifying finegrained user activities within the wireless network traffic generated by apps. Smartphone apps have changed the way we interact with online services, but highly specialized apps come at a cost to privacy.
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