Identifying User Actions from HTTP(S) Traffic

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 per-connection 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