Uncovering Fingerprinting Networks. An Analysis of In-Browser Tracking using a Behavior-based Approach
Sebastian Neef

TL;DR
This paper presents FPNET, a behavior-based tool for identifying browser fingerprinting scripts on websites, revealing widespread fingerprinting networks and raising privacy concerns.
Contribution
The study introduces FPNET, a scalable, reliable method to detect fingerprinting scripts based on behavior, and provides a comprehensive analysis of fingerprinting networks on top websites.
Findings
Identified several hundred fingerprinting networks on top websites.
Successfully re-identified 86% of scripts despite URL changes.
Most fingerprinting scripts use TLS/SSL and security headers.
Abstract
Throughout recent years, the importance of internet-privacy has continuously risen. [...] Browser fingerprinting is a technique that does not require cookies or persistent identifiers. It derives a sufficiently unique identifier from the various browser or device properties. Academic work has covered offensive and defensive fingerprinting methods for almost a decade, observing a rise in popularity. This thesis explores the current state of browser fingerprinting on the internet. For that, we implement FPNET - a scalable & reliable tool based on FPMON, to identify fingerprinting scripts on large sets of websites by observing their behavior. By scanning the Alexa Top 10,000 websites, we spot several hundred networks of equally behaving scripts. For each network, we determine the actor behind it. We track down companies like Google, Yandex, Maxmind, Sift, or FingerprintJS, to name a few.…
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Taxonomy
TopicsHate Speech and Cyberbullying Detection · Internet Traffic Analysis and Secure E-voting · Spam and Phishing Detection
