Private Web Browsing | ScienceGate

Learning from a User's Web Browsing Behavior

PsycEXTRA Dataset ◽

10.1037/e575782012-011 ◽

1999 ◽

Author(s):

Mary Czerwinksi

Keyword(s):

Web Browsing ◽

Browsing Behavior

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Clickprints on the Web: Are There Signatures in Web Browsing Data?

SSRN Electronic Journal ◽

10.2139/ssrn.931057 ◽

2007 ◽

Cited By ~ 10

Author(s):

Balaji Padmanabhan ◽

Yinghui (Catherine) Yang

Keyword(s):

Web Browsing ◽

The Web

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Characterizing Embedded Web Browsing in Mobile Apps

IEEE Transactions on Mobile Computing ◽

10.1109/tmc.2021.3065945 ◽

2021 ◽

pp. 1-1

Author(s):

Deyu Tian ◽

Yun Ma ◽

Aruna Balasubramanian ◽

Yunxin Liu ◽

Gang Huang ◽

...

Keyword(s):

Mobile Apps ◽

Web Browsing ◽

Embedded Web

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Improving Web QoE Monitoring for Encrypted Network Traffic through Time Series Modeling

ACM SIGMETRICS Performance Evaluation Review ◽

10.1145/3466826.3466840 ◽

2021 ◽

Vol 48 (4) ◽

pp. 37-40

Author(s):

Nikolas Wehner ◽

Michael Seufert ◽

Joshua Schuler ◽

Sarah Wassermann ◽

Pedro Casas ◽

...

Keyword(s):

Neural Networks ◽

Time Series ◽

Recurrent Neural Networks ◽

Quality Of Experience ◽

Unique Feature ◽

Time Series Modeling ◽

Web Browsing ◽

Large Dataset ◽

Efficient Approximation

This paper addresses the problem of Quality of Experience (QoE) monitoring for web browsing. In particular, the inference of common Web QoE metrics such as Speed Index (SI) is investigated. Based on a large dataset collected with open web-measurement platforms on different device-types, a unique feature set is designed and used to estimate the RUMSI - an efficient approximation to SI, with machinelearning based regression and classification approaches. Results indicate that it is possible to estimate the RUMSI accurately, and that in particular, recurrent neural networks are highly suitable for the task, as they capture the network dynamics more precisely.

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