融合语义和共现特征的Web跟踪器深度识别方法

doi:10.11871/jfdc.issn.2096-742X.2024.03.014

数据与计算发展前沿 ›› 2024, Vol. 6 ›› Issue (3): 127-138.

CSTR: 32002.14.jfdc.CN10-1649/TP.2024.03.014

doi: 10.11871/jfdc.issn.2096-742X.2024.03.014

融合语义和共现特征的Web跟踪器深度识别方法

严瑾(),董科军,李洪涛^*()

中国互联网络信息中心，北京 100190

收稿日期:2023-03-07 出版日期:2024-06-20 发布日期:2024-06-21
通讯作者: *李洪涛（E-mail: lihongtao@cnnic.cn）
作者简介:严瑾，中国互联网络信息中心，助理工程师，主要研究方向为域名系统。
本文中负责初稿撰写与方法实现验证。YAN Jin is an assistant engineer in China Internet Network Information Center. Her research interest is Domain Name System.
In this paper, she is responsible for the paper drafting as well as the development and experiments.
E-mail: yanjin@cnnic.cn|李洪涛，中国互联网络信息中心总工程师,正高级工程师，研究领域为计算机应用技术、下一代互联网架构，当前主要从事互联网基础资源新型解析技术及大数据分析研究。
本文中负责方法总体策划，论文修改、审定。
LI Hongtao is a professor in China Internet Network Information Center, where he is a chief engineer. His research interests include computer application technology and next-generation Internet architecture. He currently focuses on new resolution techniques and big data analytics for Internet resources
In this paper, he is responsible for the design of the method and the revision of the paper.
E-mail: lihongtao@cnnic.cn
基金资助:
国家重点研发计划课题“互联网基础设施关键信息分析技术”(2022YFB3105003)

A Deep Web Tracker Detection Method with Coordinated Semantic and Co-Occurrence Features

YAN Jin(),DONG Kejun,LI Hongtao^*()

China Internet Network Information Center, Beijing 100190, China

Received:2023-03-07 Online:2024-06-20 Published:2024-06-21

摘要/Abstract

摘要：

【目的】Web跟踪器通过嵌入用户访问的网站，收集用户的标识与访问信息，用于个性化推荐服务和网站性能分析等。然而，Web跟踪器对互联网用户来说可能会造成隐私泄漏，让用户有选择的关闭/打开Web跟踪对互联网健康发展至关重要，而Web跟踪器的自动识别是前提与基础。【方法】通过对实际数据的分析，发现Web跟踪器在URL的文本语义和嵌入关联（即共现）两个维度的重要特征，并据此设计了融合关联特征与语义特征的Web跟踪器深度识别方法。该方法首先建立用户直接访问网站和其嵌入URL的嵌入关系二部图，并基于DeepWalk算法提取URL的嵌入特征向量；其次，基于自然语言处理领域的预训练BERT模型提取URL字符串的文本语义特征；最后，使用注意力机制聚合两类特征，并使用多层感知机模型实现URL的分类，识别Web跟踪器。【结果】基于真实数据的实验结果表明，与已有方法相比，本文所提方法提高了识别的准确度，其F1分数可达到0.91。【结论】基于深度学习的Web跟踪器识别方法仅依赖跟踪器URL及其在网站的嵌入关系信息，取得了较高的识别准确度，易于部署。

关键词: Web跟踪器识别, 关联特征, 深度学习, 预训练模型, 域名系统

Abstract:

[Objective] Web trackers embedded in the website can collect the user identification and access information from user’s visit. The collected information may be used for personalized recommendation services and website performance analysis. However, web trackers may also cause Internet users privacy leakages. It is very important to allow users to selectively turn off/on web tracking, where the automatic detection of web trackers is the premise and foundation. [Methods] By analyzing real-life data sets, this paper reveals two important characteristics of web trackers from the perspectives of URL text semantics and embedded association (i.e., co-occurrence). With this basis, this paper designs a web tracker detection method based on deep learning that consolidates the semantic features and association features of URLs. Specifically, the method first constructs the bipartite graph of the embedding relationship between the websites that users visit directly and the embedded URLs of the websites, and then extracts the embedded feature vector of the URL by applying the DeepWalk algorithm. Secondly, the method extracts the text semantic features of the URL strings using the pre-trained BERT model in the field of natural language processing. Finally, the method uses the attention mechanism to consolidate the two types of features and uses the multi-layer perceptron model to implement URL classification and identify Web trackers. [Results] Experimental results based on real-life data sets show that compared with existing methods, the proposed method improves the recognition accuracy, and its F1 score can reach 0.91. [Conclusions] The proposed method achieves relatively high accuracy in detecting trackers by using only the URLs of trackers and their embedding information in websites. As such, it is easy to be deployed in practice.

Key words: Web tracker detection, association features, deep learning, pre-trained model, DNS

严瑾, 董科军, 李洪涛. 融合语义和共现特征的Web跟踪器深度识别方法[J]. 数据与计算发展前沿, 2024, 6(3): 127-138.

YAN Jin, DONG Kejun, LI Hongtao. A Deep Web Tracker Detection Method with Coordinated Semantic and Co-Occurrence Features[J]. Frontiers of Data and Computing, 2024, 6(3): 127-138, https://cstr.cn/32002.14.jfdc.CN10-1649/TP.2024.03.014.

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参考文献 30

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表名	字段名	含义
domain	domain id	用户直接访问的网站域名标识
domain	domain name	网站域名字符串
url	url id	唯一标识嵌入到网站的URL
	url string	嵌入到网站的URL字符串
	tracker label	URL是否为Web跟踪器
domain_url	<domain id, url id>	URL嵌入了domain对应的网站

	Domain数	URL数	跟踪器数	二部图边数
top1,000	1,000	87,308	21,672	99,562
top5,000	5,000	392,249	93,660	453,330
top10,000	10,000	725,229	165,081	886,194
top20,000	20,000	1，338,688	290，469	1，656,905

指标	描述
TP	被正确识别的跟踪器数
FP	被识别跟踪器的正常URL数
TN	被正确识别的正常URL数
FN	被识别为正常的跟踪器数
准确率	被正确识别的跟踪器比例：（TP+TN）/（TP+FP+TN+FN）
精确率	识别为跟踪器的样本中真实跟踪器的比例：TP/（TP+FP）
召回率	真实跟踪器中被正确识别为跟踪器的样本比例：TP/（TP+FN）
F1分数	2精确率召回率/（精确率+召回率）

数据集	方法	准确率	精确率	召回率	F1分数
top1,000	决策树	0.766,7	0.768,1	0.771,4	0.769,7
	随机森林	0.825,0	0.832,3	0.901,4	0.865,5
	逻辑回归	0.785,7	0.793,1	0.845,2	0.818,3
	Label Propagation	0.759,2	0.568,5	0.201,1	0.297,1
	本方法	0.879,3	0.852,5	0.912,9	0.881,7
top5,000	决策树	0.790,0	0.789,6	0.784,9	0.787,3
	随机森林	0.853,4	0.857,0	0.898,5	0.877,2
	逻辑回归	0.814,2	0.818,5	0.856,3	0.837,0
	Label Propagation	0.779,2	0.661,0	0.203,0	0.310,6
	本方法	0.906,1	0.891,1	0.924,5	0.907,5
top10,000	决策树	0.788,6	0.790,0	0.797,2	0.793,5
	随机森林	0.857,2	0.860,9	0.906,1	0.883,0
	逻辑回归	0.813,6	0.818,2	0.859,4	0.838,3
	Label Propagation	0.790,0	0.644,7	0.199,1	0.304,2
	本方法	0.910,4	0.890,2	0.936,6	0.912,8
top20,000	决策树	0.799,6	0.800,7	0.805,8	0.803,2
	随机森林	0.863,0	0.866,3	0.907,5	0.886,4
	逻辑回归	0.817,6	0.822,4	0.864,5	0.842,9
	Label Propagation	0.795,8	0.624,3	0.174,8	0.273,1
	本方法	0.910,9	0.887,6	0.941,4	0.913,7

学习率	准确率	精确率	召回率	F1分数
1e-2	0.669,5	0.677,0	0.643,0	0.659,6
1e-3	0.910,9	0.887,6	0.941,4	0.913,7
1e-4	0.901,0	0.892,1	0.911,3	0.901,6
1e-5	0.856,0	0.823,2	0.905,3	0.862,3

融合语义和共现特征的Web跟踪器深度识别方法

A Deep Web Tracker Detection Method with Coordinated Semantic and Co-Occurrence Features

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摘要/Abstract

引用本文

使用本文

图/表 10

参考文献 30

相关文章 15

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本文评价

批大小	准确率	精确率	召回率	F1分数
32	0.887,3	0.871,1	0.908,2	0.889,2
64	0.903,1	0.885,8	0.924,5	0.904,8
128	0.910,9	0.887,6	0.941,4	0.913,7
256	0.903,3	0.884,3	0.927,3	0.905,3

	准确率	精确率	召回率	F1分数
w/o name	0.677,7	0.690,0	0.640,5	0.664,3
w/o net	0.894,6	0.874,6	0.920,2	0.896,8
w/o att	0.895,5	0.885,2	0.907,9	0.896,4
w/o mlp	0.891,2	0.872,0	0.916,0	0.893,4
本文所提方法	0.910,9	0.887,6	0.941,4	0.913,7

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