WTAGRAPH: Web Tracking and Advertising Detection using Graph Neural Networks
WTAGRAPH: Web Tracking and Advertising Detection using Graph Neural Networks
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DOI：
10.5281/zenodo.5166790
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发表时间：
2021-08
期刊：
影响因子：
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通讯作者：
Zhiju Yang;Weiping Pei;Mon-Chu Chen;Chuan Yue
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文献类型：
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作者：
Zhiju Yang;Weiping Pei;Mon-Chu Chen;Chuan Yue
网络跟踪和广告（WTA）如今在网络上无处不在，不断损害用户的隐私。现有的防御解决方案，例如广泛部署的基于过滤器列表的拦截工具和先前研究中提出的基于机器学习的替代解决方案，在准确性和有效性方面存在局限性。在这项工作中，我们提出了WTAGRAPH，一个基于图神经网络（GNNs）的网络跟踪和广告检测框架。首先，我们构建了一个属性同质多图（AHMG），表示HTTP网络流量，并制定网页跟踪和广告检测作为AHMG基于GNN的边表示学习和分类的任务。然后，我们在WTAGRAPH中设计了四个组件，以便它可以（1）收集HTTP网络流量，DOM和JavaScript数据，（2）构建AHMG并提取相应的边缘和节点特征，（3）构建GNN模型用于直推学习设置中的边缘表示学习和WTA检测，以及（4）使用预训练的GNN模型用于归纳学习设置中的WTA检测。我们在从Alexa Top 10K网站收集的数据集上评估了WTAGRAPH，并表明WTAGRAPH可以有效地检测WTA请求，无论是在传导学习还是在归纳学习环境中。手动验证结果表明，WTAGRAPH可以检测到过滤器列表遗漏的新WTA请求，并识别过滤器列表错误标记的非WTA请求。我们的消融分析、规避评估和实时评估表明，WTAGRAPH在实践中具有灵活的部署选项，具有竞争力的性能。
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.