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Poster communications

How Good is your Mobile (Web) Surfing? Speed Index Inference from Encrypted Traffic

Abstract : We address the problem of Web QoE monitoring, in particular Speed Index (SI), from the Internet Service Provider (ISP) perspective, relying on in-network, passive measurements. Given the wide adoption of end-to-end encryption, we resort to machine-learning models to infer the SI of individual web-page loading sessions, using as input only packet-level data. Our study targets the analysis of SI in mobile devices, including smartphones and tablets. To the best of our knowledge, this is the first paper addressing the inference of SI from encrypted network traffic in mobile devices.
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Contributor : Sarah Wassermann Connect in order to contact the contributor
Submitted on : Monday, September 7, 2020 - 11:41:20 PM
Last modification on : Monday, November 29, 2021 - 3:44:08 PM
Long-term archiving on: : Saturday, December 5, 2020 - 12:28:30 AM


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Sarah Wassermann, Pedro Casas, Michael Seufert, Nikolas Wehner, Joshua Schuler, et al.. How Good is your Mobile (Web) Surfing? Speed Index Inference from Encrypted Traffic. ACM SIGCOMM 2020 Posters, Demos, and Student Research Competition, Aug 2020, New York, United States. ⟨10.1145/3405837.3411382⟩. ⟨hal-02932838⟩



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