Browser fingerprinting consists in collecting attributes from a web browser to build a browser fingerprint. In this work, we assess the adequacy of browser fingerprints as an authentication factor, on a dataset of 4, 145, 408 fingerprints composed of 216 attributes. It was collected throughout 6 months from a population of general browsers. We identify, formalize, and assess the properties for browser fingerprints to be usable and practical as an authentication factor. We notably evaluate their distinctiveness, their stability through time, their collection time, and their size in memory. We show that considering a large surface of 216 fingerprinting attributes leads to an unicity rate of 81% on a population of 1, 989, 365 browsers. Moreover, browser fingerprints are known to evolve, but we observe that between consecutive fingerprints, more than 90% of the attributes remain unchanged after nearly 6 months. Fingerprints are also affordable. On average, they weigh a dozen of kilobytes, and are collected in a few seconds. We conclude that browser fingerprints are a promising additional web authentication factor.

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 article, 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 six 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 their contribution to the evaluated properties. We conclude that our browser fingerprints carry the promise to strengthen web authentication mechanisms.

Browser fingerprinting consists into collecting attributes from a web browser. Hundreds of attributes have been discovered through the years. Each one of them provides a way to distinguish browsers, but also comes with a usability cost (e.g., additional collection time). In this work, we propose FPSelect, an attribute selection framework allowing verifiers to tune their browser fingerprinting probes for web authentication. We formalize the problem as searching for the attribute set that satisfies a security requirement and minimizes the usability cost. The security is measured as the proportion of impersonated users given a fingerprinting probe, a user population, and an attacker that knows the exact fingerprint distribution among the user population. The usability is quantified by the collection time of browser fingerprints, their size, and their instability. We compare our framework with common baselines, based on a real-life fingerprint dataset, and find out that in our experimental settings, our framework selects attribute sets of lower usability cost. Compared to the baselines, the attribute sets found by FPSelect generate fingerprints that are up to 97 times smaller, are collected up to 3, 361 times faster, and with up to 7.2 times less changing attributes between two observations, on average.

In this demonstration, we put ourselves in the place of a website manager who seeks to use browser fingerprinting for web authentication. The first step is to choose the attributes to implement among the hundreds that are available. To do so, we developed BrFAST, an attribute selection platform that includes FPSelect, an algorithm that rigorously selects the attributes according to a tradeoff between security and usability. BrFAST is configured with a set of parameters for which we provide values for BrFAST to be usable as is. We notably include the resources to use two publicly available browser fingerprint datasets. BrFAST can be extended to use other parameters: other attribute selection methods, other measures of security and usability, or other fingerprint datasets. BrFAST helps visualize the exploration of the possibilities during the search of the best attribute set to use, evaluate the properties of attribute sets, and compare several attribute selection methods. During the demonstration, we compare the attribute sets selected by FPSelect with those selected by the usual methods according to the properties of the resulting browser fingerprints (e.g., their usability, their unicity). CCS CONCEPTS• Security and privacy → Multi-factor authentication; • Information systems → Browsers.