PROJECT TITLE :
Analyzing Android Encrypted Network Traffic to Identify User Actions
Mobile devices can be maliciously exploited to violate the privacy of people. In most attack situations, the adversary takes the local or remote control of the mobile device, by leveraging a vulnerability of the system, hence sending back the collected data to some remote web service. During this paper, we have a tendency to consider a different adversary, who will not interact actively with the mobile device, but he is in a position to eavesdrop the network traffic of the device from the network side (e.g., controlling a Wi-Fi access purpose). The very fact that the network traffic is typically encrypted makes the attack even a lot of challenging. In this paper, we have a tendency to investigate to what extent such an external attacker can establish the precise actions that a user is performing on her mobile apps. We tend to design a system that achieves this goal using advanced machine learning techniques. We have a tendency to designed a whole implementation of this method, and we have a tendency to also run a thorough set of experiments, which show that our attack will achieve accuracy and precision over 95%, for most of the thought of actions. We tend to compared our answer with the three state-of-the-art algorithms, and confirming that our system outperforms all these direct competitors.
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