Beyond the Request: Harnessing HTTP Response Headers for Cross-Browser Web Tracker Classification in an Imbalanced Setting

TL;DR

This paper explores using HTTP response headers and machine learning to detect web trackers across different browsers, highlighting high accuracy in some cases but challenges with others due to data distribution differences.

Contribution

It introduces a novel approach utilizing HTTP response headers for web tracker classification and evaluates multiple machine learning models across different browsers and datasets.

Findings

High accuracy in Chrome and Firefox detection

Poor performance on Brave due to data distribution differences

Viability of classifiers for web tracker detection

Abstract

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.