Actions speak louder than words: Semi-supervised ...
Type de document :
Pré-publication ou Document de travail
Titre :
Actions speak louder than words: Semi-supervised learning for browser fingerprinting detection
Auteur(s) :
Bird, Sarah [Auteur]
Mozilla [Paris]
Mishra, Vikas [Auteur]
Self-adaptation for distributed services and large software systems [SPIRALS]
Englehardt, Steven [Auteur]
Mozilla [Paris]
Willoughby, Rob [Auteur]
Mozilla [Paris]
Zeber, David [Auteur]
Mozilla [Paris]
Rudametkin, Walter [Auteur]
Self-adaptation for distributed services and large software systems [SPIRALS]
Lopatka, Martin [Auteur]
Mozilla [Paris]
Mozilla [Paris]
Mishra, Vikas [Auteur]
Self-adaptation for distributed services and large software systems [SPIRALS]
Englehardt, Steven [Auteur]
Mozilla [Paris]
Willoughby, Rob [Auteur]
Mozilla [Paris]
Zeber, David [Auteur]
Mozilla [Paris]
Rudametkin, Walter [Auteur]
Self-adaptation for distributed services and large software systems [SPIRALS]
Lopatka, Martin [Auteur]
Mozilla [Paris]
Discipline(s) HAL :
Informatique [cs]/Cryptographie et sécurité [cs.CR]
Résumé en anglais : [en]
As online tracking continues to grow, existing anti-tracking and fingerprinting detection techniques that require significant manual input must be augmented. Heuristic approaches to fingerprinting detection are precise but ...
Lire la suite >As online tracking continues to grow, existing anti-tracking and fingerprinting detection techniques that require significant manual input must be augmented. Heuristic approaches to fingerprinting detection are precise but must be carefully curated. Supervised machine learning techniques proposed for detecting tracking require manually generated label-sets. Seeking to overcome these challenges, we present a semi-supervised machine learning approach for detecting fingerprinting scripts. Our approach is based on the core insight that fingerprinting scripts have similar patterns of API access when generating their fingerprints, even though their access patterns may not match exactly. Using this insight, we group scripts by their JavaScript (JS) execution traces and apply a semi-supervised approach to detect new fingerprinting scripts. We detail our methodology and demonstrate its ability to identify the majority of scripts ($\geqslant$94.9%) identified by existing heuristic techniques. We also show that the approach expands beyond detecting known scripts by surfacing candidate scripts that are likely to include fingerprinting. Through an analysis of these candidate scripts we discovered fingerprinting scripts that were missed by heuristics and for which there are no heuristics. In particular, we identified over one hundred device-class fingerprinting scripts present on hundreds of domains. To the best of our knowledge, this is the first time device-class fingerprinting has been measured in the wild. These successes illustrate the power of a sparse vector representation and semi-supervised learning to complement and extend existing tracking detection techniques.Lire moins >
Lire la suite >As online tracking continues to grow, existing anti-tracking and fingerprinting detection techniques that require significant manual input must be augmented. Heuristic approaches to fingerprinting detection are precise but must be carefully curated. Supervised machine learning techniques proposed for detecting tracking require manually generated label-sets. Seeking to overcome these challenges, we present a semi-supervised machine learning approach for detecting fingerprinting scripts. Our approach is based on the core insight that fingerprinting scripts have similar patterns of API access when generating their fingerprints, even though their access patterns may not match exactly. Using this insight, we group scripts by their JavaScript (JS) execution traces and apply a semi-supervised approach to detect new fingerprinting scripts. We detail our methodology and demonstrate its ability to identify the majority of scripts ($\geqslant$94.9%) identified by existing heuristic techniques. We also show that the approach expands beyond detecting known scripts by surfacing candidate scripts that are likely to include fingerprinting. Through an analysis of these candidate scripts we discovered fingerprinting scripts that were missed by heuristics and for which there are no heuristics. In particular, we identified over one hundred device-class fingerprinting scripts present on hundreds of domains. To the best of our knowledge, this is the first time device-class fingerprinting has been measured in the wild. These successes illustrate the power of a sparse vector representation and semi-supervised learning to complement and extend existing tracking detection techniques.Lire moins >
Langue :
Anglais
Collections :
Source :
Fichiers
- http://arxiv.org/pdf/2003.04463
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- 2003.04463
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