论文标题
指纹指纹：学习检测浏览器指纹行为
Fingerprinting the Fingerprinters: Learning to Detect Browser Fingerprinting Behaviors
论文作者
论文摘要
浏览器指纹是一种侵入性且不透明的无状态跟踪技术。浏览器供应商，学术界和标准机构长期以来一直在努力提供有意义的保护措施，以防止浏览器指纹识别既准确又不会降低用户体验。我们提出了FP-insector，这是一种基于机器学习的句法语义方法，可准确检测浏览器指纹。我们表明，FP-Anspector表现良好，使我们能够比最先进的指纹脚本检测到26％。我们表明，基于FP检查员的API级指纹对策有助于将网站损坏减少2倍。我们使用FP检查员对TOP-100K网站上的浏览器指纹进行测量研究。我们发现，现在超过10％的前100k个网站和超过四分之一的前10k网站上存在浏览器指纹。我们还通过指纹脚本提示他们正在寻求以新的和意外的方式利用API来发现以前未报告的JavaScript API的用途。
Browser fingerprinting is an invasive and opaque stateless tracking technique. Browser vendors, academics, and standards bodies have long struggled to provide meaningful protections against browser fingerprinting that are both accurate and do not degrade user experience. We propose FP-Inspector, a machine learning based syntactic-semantic approach to accurately detect browser fingerprinting. We show that FP-Inspector performs well, allowing us to detect 26% more fingerprinting scripts than the state-of-the-art. We show that an API-level fingerprinting countermeasure, built upon FP-Inspector, helps reduce website breakage by a factor of 2. We use FP-Inspector to perform a measurement study of browser fingerprinting on top-100K websites. We find that browser fingerprinting is now present on more than 10% of the top-100K websites and over a quarter of the top-10K websites. We also discover previously unreported uses of JavaScript APIs by fingerprinting scripts suggesting that they are looking to exploit APIs in new and unexpected ways.
