Today’s mobile apps employ third-party advertising and tracking (A&T) libraries, which may pose a threat to privacy. State-of-the-art detects and blocks outgoing A&T HTTP/S requests by using manually curated filter lists (e.g. EasyList), and recently, using machine learning approaches. The major bottleneck of both filter lists and classifiers is that they rely on experts and the community to inspect traffic and manually create filter list rules that can then be used to block traffic or label ground truth datasets. We propose NoMoATS – a system that removes this bottleneck by reducing the daunting task of manually creating filter rules, to the much easier and scalable task of labeling A&T libraries. Our system leverages stack trace analysis to automatically label which network requests are generated by A&T libraries. Using NoMoATS, we collect and label a new mobile traffic dataset. We use this dataset to train decision tree classifiers, which can be applied in real-time on the mobile device and achieve
an average F-score of 93%. We show that both our automatic labeling and our classifiers discover thousands of requests destined to hundreds of different hosts, previously undetected by popular filter lists. To the best of our knowledge, our system is the first to (1) automatically label which mobile network requests are engaged in A&T, while requiring to only manually label libraries to their purpose and (2) apply on-device machine learning classifiers that operate at the granularity of URLs, can inspect connections across all apps, and detect not only ads, but also tracking.
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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 real-time evaluation show that WtaGraph can have a competitive performance with flexible deployment options in practice.
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- Award ID(s):
- 1936968
- NSF-PAR ID:
- 10287982
- Date Published:
- Journal Name:
- IEEE Symposium on Security and Privacy
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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