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As online tracking continues to grow, existing anti-tracking andfingerprinting detection techniques that require significant manual input mustbe augmented. Heuristic approaches to fingerprinting detection are precise butmust be carefully curated. Supervised machine learning techniques proposed fordetecting tracking require manually generated label-sets. Seeking to overcomethese challenges, we present a semi-supervised machine learning approach fordetecting fingerprinting scripts. Our approach is based on the core insightthat fingerprinting scripts have similar patterns of API access when generatingtheir fingerprints, even though their access patterns may not match exactly.Using this insight, we group scripts by their JavaScript (JS) execution tracesand apply a semi-supervised approach to detect new fingerprinting scripts. Wedetail our methodology and demonstrate its ability to identify the majority ofscripts ($\geqslant$94.9%) identified by existing heuristic techniques. We alsoshow that the approach expands beyond detecting known scripts by surfacingcandidate scripts that are likely to include fingerprinting. Through ananalysis of these candidate scripts we discovered fingerprinting scripts thatwere missed by heuristics and for which there are no heuristics. In particular,we identified over one hundred device-class fingerprinting scripts present onhundreds of domains. To the best of our knowledge, this is the first timedevice-class fingerprinting has been measured in the wild. These successesillustrate the power of a sparse vector representation and semi-supervisedlearning to complement and extend existing tracking detection techniques.

Actions speak louder than words: Semi-supervised learning for browser fingerprinting detection