MVC-Corr:一种基于多视图融合和对比度增强的准确高效的流相关方法

IF 4.4 2区 计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE
YuKuan Tu, Tengyao Li, Meng Zhang, Xiangyang Luo
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引用次数: 0

摘要

流量关联攻击通过分析客户端与服务之间数据流的行为相似性,确定在多中继加密流量条件下客户端与服务之间的访问关系。然而,当前流量相关攻击的有效性容易受到流量混淆的影响,并且训练效率较低,这极大地限制了其在Tor上的应用。针对这一问题,本文提出了一种基于多视图融合和对比度增强的准确高效的流量关联方法MVC-Corr。首先,设计了多视图融合特征提取网络(MVF)。网络集成了上下行交互视图、本地视图和全局视图三种视图,实现了精确的特征提取。其次,提出了一种偏置交叉对比度增强机制。该机制生成了具有丰富对比信息的非相关流特征对,提高了相关分析的效率。最后,为了增强所提方法在不同目标尺度上的广泛适用性,MVC-Corr设计了两种运行模式,分别针对用户跟踪场景和用户发现场景进行了定制。实验结果表明,在用户跟踪场景中,MVC-Corr在准确性方面优于现有的三种典型方法——deepcorr、FlowTracker和restore,在流量混淆条件下,其准确率提高了13.3%至30.9%。在用户发现场景中,实验结果表明,MVC-Corr的相关能力超过了目前最先进的方法DeepCoFFEA,最大真阳性率提高了2.9%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
MVC-Corr: An accurate and efficient flow correlation method based on multi-view fusion and contrast augmentation
Flow correlation attacks determine the access relationship between clients and services under conditions of multi-relay encrypted traffic by analyzing the behavior similarity of their data flows. However, the effectiveness of current flow correlation attacks is susceptible to traffic obfuscation and suffers from low training efficiency, which greatly restricts their application on Tor. To address this, the paper proposes an accurate and efficient flow correlation method named MVC-Corr, which is based on multi-view fusion and contrast augmentation. Firstly, a multi-view fusion feature extraction network (MVF) is designed. The network integrates three types of views: uplink–downlink interaction view, local view, and global view, to achieve precise feature extraction. Secondly, an offset intersection contrast augmentation mechanism (ICA) is developed. The mechanism generates non-correlated flow feature pairs with abundant contrast information, improving the efficiency of correlation analysis. Finally, to enhance the broad applicability of the proposed method across different target scales, MVC-Corr is designed with two operational modes, each tailored for the user tracking scenario and the user discovery scenario. The experimental results show that, in user tracking scenario, MVC-Corr outperforms three existing typical methods—DeepCorr, FlowTracker, and ResTor—in terms of accuracy, achieving improvements ranging from 13.3% to 30.9% under traffic obfuscation conditions. In user discovery scenario, experimental results demonstrate that MVC-Corr’s correlation capability surpasses that of the current state-of-the-art method, DeepCoFFEA, achieving a maximum true positive rate improvement of 2.9%.
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来源期刊
Computer Networks
Computer Networks 工程技术-电信学
CiteScore
10.80
自引率
3.60%
发文量
434
审稿时长
8.6 months
期刊介绍: Computer Networks is an international, archival journal providing a publication vehicle for complete coverage of all topics of interest to those involved in the computer communications networking area. The audience includes researchers, managers and operators of networks as well as designers and implementors. The Editorial Board will consider any material for publication that is of interest to those groups.
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