论文标题

Web身份验证的浏览器指纹属性的大规模经验分析

A Large-scale Empirical Analysis of Browser Fingerprints Properties for Web Authentication

论文作者

Andriamilanto, Nampoina, Allard, Tristan, Guelvouit, Gaëtan Le, Garel, Alexandre

论文摘要

现代浏览器可访问几种可以收集的属性以形成浏览器指纹。尽管浏览器指纹主要是作为网络跟踪工具研究的,但它们可以通过增强Web身份验证机制来改善网络安全的当前状态。在本文中,我们研究了浏览器指纹用于Web身份验证的充分性。我们在区分浏览器的数字指纹和区分人类的生物指纹之间建立联系,以根据受生物识别身份验证因子启发的特性评估浏览器指纹。这些属性包括它们的独特性,稳定性,收集时间,大小以及简单验证机制的准确性。我们在4,145,408个指纹的大规模数据集上评估了这些属性,该数据集由216个属性组成,并从1,989,365个浏览器中收集。我们表明,通过分配我们的数据集,单个浏览器共享了超过81.3%的指纹。尽管已知浏览器指纹会发展,但我们指纹的平均属性的平均属性在两个观察结果之间保持相同,即使在近6个月的时间分开。关于它们的性能,我们表明我们的指纹重量十几个千字节,并需要几秒钟的时间才能收集。最后,通过处理一种简单的验证机制,我们表明它的错误率相等的0.61%。我们通过分析属性之间的相关性及其对评估特性的贡献来丰富我们的结果。我们得出的结论是,我们的浏览器指纹具有加强网络身份验证机制的承诺。

Modern browsers give access to several attributes that can be collected to form a browser fingerprint. Although browser fingerprints have primarily been studied as a web tracking tool, they can contribute to improve the current state of web security by augmenting web authentication mechanisms. In this paper, we investigate the adequacy of browser fingerprints for web authentication. We make the link between the digital fingerprints that distinguish browsers, and the biological fingerprints that distinguish Humans, to evaluate browser fingerprints according to properties inspired by biometric authentication factors. These properties include their distinctiveness, their stability through time, their collection time, their size, and the accuracy of a simple verification mechanism. We assess these properties on a large-scale dataset of 4,145,408 fingerprints composed of 216 attributes, and collected from 1,989,365 browsers. We show that, by time-partitioning our dataset, more than 81.3% of our fingerprints are shared by a single browser. Although browser fingerprints are known to evolve, an average of 91% of the attributes of our fingerprints stay identical between two observations, even when separated by nearly 6 months. About their performance, we show that our fingerprints weigh a dozen of kilobytes, and take a few seconds to collect. Finally, by processing a simple verification mechanism, we show that it achieves an equal error rate of 0.61%. We enrich our results with the analysis of the correlation between the attributes, and of their contribution to the evaluated properties. We conclude that our browser fingerprints carry the promise to strengthen web authentication mechanisms.

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