Robust smartphone app identification via encrypted network traffic analysis

Robust smartphone app identification via encrypted network traffic analysis

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The apps installed on a smartphone can reveal much information about a user, such as their medical conditions, sexual orientation, or religious beliefs. In addition, the presence or absence of particular apps on a smartphone can inform an adversary, who is intent on attacking the device. In this pap...

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Bibliografiske detaljer
Main Authors:	Taylor, V, Spolaor, R, Conti, M, Martinovic, I
Format:	Journal article
Udgivet:	Institute of Electrical and Electronics Engineers 2017

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Udgivet: (2016-09-01)

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