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

arXiv: https://arxiv.org/abs/2402.01240 · HTML: https://mlsanity.com/paper/2402.01240/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 66 references that are in the map.
- First posted on arXiv 2 Feb 2024.

## Builds on (most cited first, 8 of 8)

- [Conditional Wasserstein GAN-based Oversampling of Tabular Data for Imbalanced Learning](https://mlsanity.com/paper/2008.09202/) (2020)
- [Tranco: A Research-Oriented Top Sites Ranking Hardened Against Manipulation](https://mlsanity.com/paper/1806.01156/) (2018)
- [SMOTE-ENC: A novel SMOTE-based method to generate synthetic data for nominal and continuous features](https://mlsanity.com/paper/2103.07612/) (2021)
- [Fingerprinting the Fingerprinters: Learning to Detect Browser Fingerprinting Behaviors](https://mlsanity.com/paper/2008.04480/) (2020)
- [WebGraph: Capturing Advertising and Tracking Information Flows for Robust Blocking](https://mlsanity.com/paper/2107.11309/) (2021)
- [Percival: Making In-Browser Perceptual Ad Blocking Practical With Deep Learning](https://mlsanity.com/paper/1905.07444/) (2019)
- [A comparison of web privacy protection techniques](https://mlsanity.com/paper/1712.06850/) (2017)
- [A Longitudinal Analysis of Online Ad-Blocking Blacklists](https://mlsanity.com/paper/1906.00166/) (2019)

## Abstract (arXiv)

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.