Beyond the Request: Harnessing HTTP Response Headers for Cross-Browser
  Web Tracker Classification in an Imbalanced Setting
Wolf Rieder Philip Raschke Thomas Cory
The World Wide Web's connectivity is greatly attributed to the HTTP protocol,
with HTTP messages offering informative header fields that appeal to
disciplines like web security and privacy, especially concerning web tracking.
Despite existing research employing HTTP/S request messages to identify web
trackers, HTTP/S response headers are often overlooked. This study endeavors to
design effective machine learning classifiers for web tracker detection using
HTTP/S response headers. Data from the Chrome, Firefox, and Brave browsers,
obtained through the traffic monitoring browser extension T.EX, serves as our
data set. Eleven supervised models were trained on Chrome data and tested
across all browsers. The results demonstrated high accuracy, F1-score,
precision, recall, and minimal log-loss error for Chrome and Firefox, but
subpar performance on Brave, potentially due to its distinct data distribution
and feature set. The research suggests that these classifiers are viable for
detecting web trackers in Chrome and Firefox. However, real-world application
testing remains pending, and the distinction between tracker types and broader
label sources could be explored in future studies.