Khaleesi: Breaker of Advertising and Tracking Request Chains


Umar Iqbal, University of Washington; Charlie Wolfe, University of Iowa; Charles Nguyen, University of California, Davis; Steven Englehardt, DuckDuckGo; Zubair Shafiq, University of California, Davis


Request chains are being used by advertisers and trackers for information sharing and circumventing recently introduced privacy protections in web browsers. There is little prior work on mitigating the increasing exploitation of request chains by advertisers and trackers. The state-of-the-art ad and tracker blocking approaches lack the necessary context to effectively detect advertising and tracking request chains. We propose Khaleesi, a machine learning approach that captures the essential sequential context needed to effectively detect advertising and tracking request chains. We show that Khaleesi achieves high accuracy, that holds well over time, is generally robust against evasion attempts, and outperforms existing approaches. We also show that Khaleesi is suitable for online deployment and it improves page load performance.

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@inproceedings {279944,
author = {Umar Iqbal and Charlie Wolfe and Charles Nguyen and Steven Englehardt and Zubair Shafiq},
title = {Khaleesi: Breaker of Advertising and Tracking Request Chains},
booktitle = {31st USENIX Security Symposium (USENIX Security 22)},
year = {2022},
isbn = {978-1-939133-31-1},
address = {Boston, MA},
pages = {2911--2928},
url = {},
publisher = {USENIX Association},
month = aug

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