Web authentication is the verification that a visitor claiming an account legitimately owns this account. Several authentication factors were proposed such that each one provides a supplementary security barrier. Browser fingerprints notably came out as a promising candidate. They are the aggregation of properties collected from a web browser, which compose a potentially unique fingerprint. In this thesis, we provide two contributions. 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. We assess these properties on four real-life browser fingerprint datasets, which include one of nearly two million browsers. We propose FPSelect, an attribute selection framework to find the attribute set that satisfies a security requirement and reduces the usability cost. The security is measured as the proportion of impersonated users given a fingerprinting probe, a user population, and a modeled attacker. The usability is quantified by the collection time of browser fingerprints, their size, and their instability. We compare our framework with common baselines using on two real-life fingerprint datasets.