Towards Seamless Tracking-Free Web: Improved Detection of Trackers via One-class Learning.

Numerous tools have been developed to aggressively block the execution of popular JavaScript programs (JS) in Web browsers. Such blocking also affects functionality of webpages and impairs user experience. As a consequence, many privacy preserving tools (PP-Tools) that have been developed to limit online tracking, often executed via JS, may suffer from poor performance and limited uptake. A mechanism that can isolate JS necessary for proper functioning of the website from tracking JS would thus be useful. Through the use of a manually labelled dataset composed of 2,612 JS, we show how current PP-Tools are ineffective in finding the right balance between blocking tracking JS and allowing functional JS. the best of our knowledge, this is the first study to assess the performance of current web PP-Tools. To improve this balance, we examine the two classes of JS and hypothesize that tracking JS share structural similarities that can be used to differentiate them from functional JS. The rationale of our approach is that web developers often borrow and customize existing pieces of code in order to embed tracking (resp. functional) JS into their webpages. then propose one-class machine learning classifiers using syntactic and semantic features extracted from JS. When trained only on samples of tracking JS, our classifiers achieve an accuracy of 99%, where the best of the PP-Tools achieved an accuracy of 78%. We further test our classifiers and several popular PP-Tools on a corpus of 4K websites with 135K JS. The output of our best classifier on this data is between 20 to 64% different from the PP-Tools. manually analyse a sample of the JS for which our classifier is in disagreement with all other PP-Tools, and show that our approach is not only able to enhance user web experience by correctly classifying more functional JS, but also discovers previously unknown tracking services.