Abstract
We address the problem ofWeb 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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