AST-Trans: Detecting Web Tracking using Transformer-based Deep Learning with Abstract Syntax Tree
Yong Yuan, Ziling Wei, Lin Liu, Shuhui Chen, Jinshu Su · 2024
Web tracking has become a key tool for service providers to collect online data and analyze user behaviors, raising concerns about the privacy of Internet users. In this paper, we propose a new web tracking detection method, namely AST-Trans, which detects and removes web tracking behavior using Transformer-based deep learning with abstract syntax trees. In the method, the abstract syntax tree is built for the detected website codes. Then, a sequence generation algorithm is proposed to convert tree-like code structures into one-dimensional sequences for deep models. To enhance training efficiency, we devise a reduction strategy to simplify the code tree structure by introducing equivalent nodes. After that, a Transformer-based deep learning algorithm is introduced to realize web tracking detection. By the proposed method, the exact tracking code blocks can be identified, and thus, we can implement the tracking code removal with minimum website breakage. To verify the effectiveness of the proposed method, an HTTPS proxy with AST-Trans on it is implemented to detect and remove tracking codes. We evaluate AST-Trans with the TrackSign-labeled dataset. The results show that the proposed method can detect the tracking behavior with high precision. In addition, we validate the feasibility of the method by measuring the website page breakage.