ADCPG: Classifying JavaScript Code Property Graphs with Explanations for Ad and Tracker Blocking

Advertising and tracking service (ATS) blocking has been safeguarding the privacy of millions of Internet users from privacy-invasive tracking behaviors. Previous research has proposed using a graph representation that models the structural relationships in loading web resources and then conducting ATS node classification based on this graph representation. However, these context-based ATS classification methods suffer from (1) inconsistent classification due to the varying context in which ATS resources are loaded and (2) a lack of explainability of the classification results, making it difficult to identify the code-level causes for ATS classification. We propose AdCPG, a graph neural network (GNN) framework tailored for ATS classification. Our approach focuses on classifying JavaScript ( JS) content rather than considering the loading context of web resources. Given JS files, AdCPG leverages their code property graphs (CPGs) and conducts graph classification on these CPGs that model the semantic and structural information of these JS files. To provide the explanations for ATS classification, AdCPG highlights the JS code that contributes the most to classifying the JS files into ATS using a GNN explainer. AdCPG achieved an accuracy of 98.75% on the Tranco top-10K websites, demonstrating high performance using only JS content. Upon deployment, AdCPG identified 650 JS files from 500 domains that were not detected by any ATS filter lists and previous ATS classification tools. AdCPG plays a complementary role in identifying ATS resources while providing code-level explanations, which minimizes the engineering effort required to validate ATS classification results.