TIGER: Tor Traffic Generator for Realistic Experiments

被引：0

作者：

Lopes, Daniela ^{[1
]}

Castro, Daniel ^{[1
]}

Barradas, Diogo ^{[2
]}

Santos, Nuno ^{[1
]}

机构：

[1] Univ Lisbon, INESC ID, IST, Lisbon, Portugal

[2] Univ Waterloo, Waterloo, ON, Canada

来源：

PROCEEDINGS OF THE 22ND WORKSHOP ON PRIVACY IN THE ELECTRONIC SOCIETY, WPES 2023 | 2023年

基金：

加拿大自然科学与工程研究理事会;

关键词：

Tor; traffic analysis; dataset generation; web privacy;

D O I：

10.1145/3603216.3624960

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

Tor is the most widely adopted anonymity network, helping safeguard the privacy of Internet users, including journalists and human rights activists. However, effective attacks aimed at deanonymizing Tor users' remains a significant threat. Unfortunately, evaluating the impact such attacks by collecting realistic Tor traffic without gathering real users' data poses a significant challenge. This paper introduces TIGER (Tor traffIc GEnerator for Realistic experiments), a novel framework that automates the generation of realistic Tor traffic datasets towards improving our understanding of the robustness of Tor's privacy mechanisms. To this end, TIGER allows researchers to design large-scale testbeds and collect data on the live Tor network while responsibly avoiding the need to collect real users' traffic. We motivate the usefulness of TIGER by collecting a preliminary dataset with applicability to the evaluation of traffic confirmation attacks and defenses.

引用

页码：147 / 152

页数：6

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