Paper:
Georgios Rizothanasis, Niklas Carlsson, and Aniket Mahanti,
"Identifying User Actions From HTTP(S) Traffic",
Proc. IEEE Conference on Local Computer Networks (IEEE LCN),
Dubai, Nov. 2016.
(
pdf)
Abstract:
When understanding modern web usage and providing
optimized personalized service, it is important to identify
the HTTP(S) requests directly caused by user actions like
clicks and typing web addresses. With a majority of HTTP(S)
requests being due to content that has not been explicitly
requested by a user, the problem of identifying user actions
at proxies or middleboxes becomes non-trivial. We present an
automated evaluation framework for identifying user actions
while also automatically providing a “ground truth” of the user
actions. We utilize the framework to compare the performance
of timing-based and HTTP-aware request classifiers, including
timing-based classifiers operating on both per-request and perconnection
basis to identify user actions. We emphasize the value
of diverse information used by the classifiers when comparing
identification accuracy both among classifiers and relative to
the browser-based ground truth. Our classifiers can be useful to
better understand users' web usage and connection prioritization.