  Browser fingerprinting is a stateless tracking technique that attempts to
combine information exposed by multiple different web APIs to create a unique
identifier for tracking users across the web. Over the last decade, trackers
have abused several existing and newly proposed web APIs to further enhance the
browser fingerprint. Existing approaches are limited to detecting a specific
fingerprinting technique(s) at a particular point in time. Thus, they are
unable to systematically detect novel fingerprinting techniques that abuse
different web APIs. In this paper, we propose FP-Radar, a machine learning
approach that leverages longitudinal measurements of web API usage on top-100K
websites over the last decade, for early detection of new and evolving browser
fingerprinting techniques. The results show that FP-Radar is able to early
detect the abuse of newly introduced properties of already known (e.g., WebGL,
Sensor) and as well as previously unknown (e.g., Gamepad, Clipboard) APIs for
browser fingerprinting. To the best of our knowledge, FP-Radar is also the
first to detect the abuse of the Visibility API for ephemeral fingerprinting in
the wild.
