{"title":"FDGAT-WTA:一种基于改进的图注意力网络的网络跟踪和广告动态检测模型","authors":"Yali Yuan , Runke Li , Guang Cheng","doi":"10.1016/j.jnca.2025.104178","DOIUrl":null,"url":null,"abstract":"<div><div>Web tracking and advertising (WTA) have become pervasive on the Internet, presenting significant challenges to user privacy and data security. Although current defense mechanisms, such as filter list based interceptors and machine learning methods, provide a solution, they do not perform well in complex network environments with missing features, and their large size makes both performance and overhead subject to optimization. This paper introduces FDGAT-WTA (Fine-tuning Dynamics GAT for WTA Detection), a dynamic model based on an improved graph attention network, designed for efficient WTA detection. The model constructs network traffic as a Homogeneous Directed Multigraph (HDMG) and modifies the graph attention aggregation strategy, enabling deep feature extraction and dynamic graph extension through transductive and inductive learning methods. The dynamic detection phase leverages pruning techniques to reduce computational load and memory usage. The experimental results show that compared with existing machine learning based WTA detection methods, FDGAT-WTA has improved detection performance by about 5%, reduced model overhead by about 25% under the same data scale , and can adapt to real complex network environments with partially missing features with minimal performance loss, realizing lightweight and efficient dynamic detection.</div></div>","PeriodicalId":54784,"journal":{"name":"Journal of Network and Computer Applications","volume":"241 ","pages":"Article 104178"},"PeriodicalIF":8.0000,"publicationDate":"2025-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"FDGAT-WTA: A dynamic detection model for web tracking and advertising based on improved graph attention networks\",\"authors\":\"Yali Yuan , Runke Li , Guang Cheng\",\"doi\":\"10.1016/j.jnca.2025.104178\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Web tracking and advertising (WTA) have become pervasive on the Internet, presenting significant challenges to user privacy and data security. Although current defense mechanisms, such as filter list based interceptors and machine learning methods, provide a solution, they do not perform well in complex network environments with missing features, and their large size makes both performance and overhead subject to optimization. This paper introduces FDGAT-WTA (Fine-tuning Dynamics GAT for WTA Detection), a dynamic model based on an improved graph attention network, designed for efficient WTA detection. The model constructs network traffic as a Homogeneous Directed Multigraph (HDMG) and modifies the graph attention aggregation strategy, enabling deep feature extraction and dynamic graph extension through transductive and inductive learning methods. The dynamic detection phase leverages pruning techniques to reduce computational load and memory usage. The experimental results show that compared with existing machine learning based WTA detection methods, FDGAT-WTA has improved detection performance by about 5%, reduced model overhead by about 25% under the same data scale , and can adapt to real complex network environments with partially missing features with minimal performance loss, realizing lightweight and efficient dynamic detection.</div></div>\",\"PeriodicalId\":54784,\"journal\":{\"name\":\"Journal of Network and Computer Applications\",\"volume\":\"241 \",\"pages\":\"Article 104178\"},\"PeriodicalIF\":8.0000,\"publicationDate\":\"2025-05-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Network and Computer Applications\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S108480452500075X\",\"RegionNum\":2,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, HARDWARE & ARCHITECTURE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Network and Computer Applications","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S108480452500075X","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, HARDWARE & ARCHITECTURE","Score":null,"Total":0}
引用次数: 0
摘要
网络跟踪和广告(WTA)在互联网上已经变得无处不在,对用户隐私和数据安全提出了重大挑战。虽然目前的防御机制,如基于过滤器列表的拦截器和机器学习方法,提供了一个解决方案,但它们在缺少特征的复杂网络环境中表现不佳,而且它们的大尺寸使得性能和开销都需要优化。本文介绍了一种基于改进的图注意网络的动态模型FDGAT-WTA (Fine-tuning Dynamics GAT for WTA Detection),用于高效检测WTA。该模型将网络流量构建为一个同质有向多图(HDMG),并对图的注意力聚合策略进行修改,通过转换和归纳学习方法实现深度特征提取和动态图扩展。动态检测阶段利用修剪技术来减少计算负载和内存使用。实验结果表明,与现有基于机器学习的WTA检测方法相比,FDGAT-WTA在相同数据规模下的检测性能提高了约5%,模型开销降低了约25%,能够以最小的性能损失适应部分特征缺失的真实复杂网络环境,实现轻量化、高效的动态检测。
FDGAT-WTA: A dynamic detection model for web tracking and advertising based on improved graph attention networks
Web tracking and advertising (WTA) have become pervasive on the Internet, presenting significant challenges to user privacy and data security. Although current defense mechanisms, such as filter list based interceptors and machine learning methods, provide a solution, they do not perform well in complex network environments with missing features, and their large size makes both performance and overhead subject to optimization. This paper introduces FDGAT-WTA (Fine-tuning Dynamics GAT for WTA Detection), a dynamic model based on an improved graph attention network, designed for efficient WTA detection. The model constructs network traffic as a Homogeneous Directed Multigraph (HDMG) and modifies the graph attention aggregation strategy, enabling deep feature extraction and dynamic graph extension through transductive and inductive learning methods. The dynamic detection phase leverages pruning techniques to reduce computational load and memory usage. The experimental results show that compared with existing machine learning based WTA detection methods, FDGAT-WTA has improved detection performance by about 5%, reduced model overhead by about 25% under the same data scale , and can adapt to real complex network environments with partially missing features with minimal performance loss, realizing lightweight and efficient dynamic detection.
期刊介绍:
The Journal of Network and Computer Applications welcomes research contributions, surveys, and notes in all areas relating to computer networks and applications thereof. Sample topics include new design techniques, interesting or novel applications, components or standards; computer networks with tools such as WWW; emerging standards for internet protocols; Wireless networks; Mobile Computing; emerging computing models such as cloud computing, grid computing; applications of networked systems for remote collaboration and telemedicine, etc. The journal is abstracted and indexed in Scopus, Engineering Index, Web of Science, Science Citation Index Expanded and INSPEC.