Name
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 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. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/LCN.2016.91 | 2016 IEEE 41st Conference on Local Computer Networks (LCN) |
Keywords | Field | DocType |
User action identification,Timing-based classifiers,Proxy logs,HTTP and HTTPS traffic | World Wide Web,Web usage,Computer science,Server,Computer network,Prioritization,Ground truth,The Internet | Conference |
ISSN | ISBN | Citations |
0742-1303 | 978-1-5090-2055-3 | 3 |
PageRank | References | Authors |
0.37 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Georgios Rizothanasis | 1 | 3 | 0.37 |
| Niklas Carlsson | 2 | 585 | 51.31 |
| Aniket Mahanti | 3 | 163 | 16.49 |