  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 request messages to identify web
trackers, HTTP response headers are often overlooked. This study endeavors to
design effective machine learning classifiers for web tracker detection using
binarized HTTP response headers. Data from the Chrome, Firefox, and Brave
browsers, obtained through the traffic monitoring browser extension T.EX,
serves as our dataset. Ten supervised models were trained on Chrome data and
tested across all browsers, including a Chrome dataset from a year later. 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 web tracker detection. However,
real-world application testing remains pending, and the distinction between
tracker types and broader label sources could be explored in future studies.
