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Poster Communications Year : 2020

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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Dates and versions

hal-02932838 , version 1 (07-09-2020)

Identifiers

Cite

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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