WTAGRAPH: Web Tracking and Advertising Detection using Graph Neural Networks

Web tracking and advertising (WTA) nowadays are ubiquitously performed on the web, continuously compromising users' privacy. Existing defense solutions, such as widely deployed blocking tools based on filter lists and alternative machine learning based solutions proposed in prior research, have limitations in terms of accuracy and effectiveness. In this work, we propose WTAGRAPH, a web tracking and advertising detection framework based on Graph Neural Networks (GNNs). We first construct an attributed homogenous multi-graph (AHMG) that represents HTTP network traffic, and formulate web tracking and advertising detection as a task of GNN-based edge representation learning and classification in AHMG. We then design four components in WTAGRAPH so that it can (1) collect HTTP network traffic, DOM, and JavaScript data, (2) construct AHMG and extract corresponding edge and node features, (3) build a GNN model for edge representation learning and WTA detection in the transductive learning setting, and (4) use a pre-trained GNN model for WTA detection in the inductive learning setting. We evaluate WTAGRAPH on a dataset collected from Alexa Top 10K websites, and show that WTAGRAPH can effectively detect WTA requests in both transductive and inductive learning settings. Manual verification results indicate that WTAGRAPH can detect new WTA requests that are missed by filter lists and recognize non-WTA requests that are mistakenly labeled by filter lists. Our ablation analysis, evasion evaluation, and realtime evaluation show that WTAGRAPH can have a competitive performance with flexible deployment options in practice.