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.

此研究使用来自 Chrome、Firefox 和 Brave 浏览器的数据，通过流量监测浏览器扩展 T.EX，设计了有效的机器学习分类器，用于使用 HTTP/S 响应头检测网络追踪器。结果显示，这些分类器在 Chrome 和 Firefox 上具有高准确性、F1 得分、精确度、召回率和最小对数损失错误，但在 Brave 上表现较差。这些分类器可用于检测 Chrome 和 Firefox 中的网络追踪器。然而，仍需要进行实际应用测试，并且未来的研究可以探讨追踪器类型和更广泛的标签来源之间的区别。