Peel the onion: Recognition of Android apps behind the Tor Network

In this work we show that Tor is vulnerable to app deanonymization attacks on Android devices through network traffic analysis. For this purpose, we describe a general methodology for performing an attack that allows to deanonymize the apps running on a target smartphone using Tor, which is the victim of the attack. Then, we discuss a Proof-of-Concept, implementing the methodology, that shows how the attack can be performed in practice and allows to assess the deanonymization accuracy that it is possible to achieve. While attacks against Tor anonymity have been already gained considerable attention in the context of website fingerprinting in desktop environments, to the best of our knowledge this is the first work that highlights Tor vulnerability to apps deanonymization attacks on Android devices. In our experiments we achieved an accuracy of 97%

Peel the Onion: Recognition of Android Apps Behind the Tor Network

According to Freedom on the Net 2017 report [15] more than 60% of World’s Internet users are not completely free from censorship. Solutions like Tor allow users to gain more freedom, bypassing these restrictions. For this reason they are continuously under deep observation to detect vulnerabilities that would compromise users anonymity. The aim of this work is showing that Tor is vulnerable to app deanonymization attacks on Android devices through network traffic analysis. While attacks against Tor anonymity have already gained considerable attention in the context of website fingerprinting in desktop environments, to the best of our knowledge this is the first work that addresses a similar problem on Android devices. For this purpose, we describe a general methodology for performing an attack that allows to deanonymize the apps running on a target smartphone using Tor. Then, we discuss a Proof-of-Concept, implementing the methodology, that shows how the attack can be performed in practice and allows to assess the deanonymization accuracy that it is possible to achieve. Moreover, we made the software of the Proof-of-Concept available, as well as the datasets used to evaluate it. In our extensive experimental evaluation, we achieved an accuracy of 97%

Locating Emergencies in a Campus Using Wi-Fi Access Point Association Data

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