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Users get routed: traffic correlation on tor by realistic adversaries

Authors:

Paul SyversonAuthors Info & Claims

CCS '13: Proceedings of the 2013 ACM SIGSAC conference on Computer & communications security

Pages 337 - 348

https://doi.org/10.1145/2508859.2516651

Published: 04 November 2013 Publication History

Abstract

We present the first analysis of the popular Tor anonymity network that indicates the security of typical users against reasonably realistic adversaries in the Tor network or in the underlying Internet. Our results show that Tor users are far more susceptible to compromise than indicated by prior work. Specific contributions of the paper include(1)a model of various typical kinds of users,(2)an adversary model that includes Tor network relays, autonomous systems(ASes), Internet exchange points (IXPs), and groups of IXPs drawn from empirical study,(3) metrics that indicate how secure users are over a period of time,(4) the most accurate topological model to date of ASes and IXPs as they relate to Tor usage and network configuration,(5) a novel realistic Tor path simulator (TorPS), and(6)analyses of security making use of all the above. To show that our approach is useful to explore alternatives and not just Tor as currently deployed, we also analyze a published alternative path selection algorithm, Congestion-Aware Tor. We create an empirical model of Tor congestion, identify novel attack vectors, and show that it too is more vulnerable than previously indicated.

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Cited By

Zhao HSong X(2024)TOAR: Toward Resisting AS-Level Adversary Correlation Attacks Optimal Anonymous RoutingMathematics10.3390/math1223364012:23(3640)Online publication date: 21-Nov-2024
https://doi.org/10.3390/math12233640
Zhu DTu JCai DJiang TXiahou JChen YLiu C(2024)SRFACS: A secure and robust framework for anonymous communication systemsPLOS ONE10.1371/journal.pone.031281719:12(e0312817)Online publication date: 2-Dec-2024
https://doi.org/10.1371/journal.pone.0312817
Zhai JZhang KZeng XMeng YLiu G(2024)FlowCorrGCN: Enhancing Flow Correlation Through Graph Convolutional Networks and Triplet NetworksInternational Journal of Intelligent Systems10.1155/2024/88235112024:1Online publication date: 30-Oct-2024
https://doi.org/10.1155/2024/8823511
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Index Terms

Users get routed: traffic correlation on tor by realistic adversaries
1. Security and privacy
  1. Network security

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Information & Contributors

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Published In

cover image ACM Conferences

CCS '13: Proceedings of the 2013 ACM SIGSAC conference on Computer & communications security

November 2013

1530 pages

ISBN:9781450324779

DOI:10.1145/2508859

General Chair:
Ahmad-Reza Sadeghi
TU Darmstadt, CASED, Intel ICRI-SC, Germany
,
Program Chairs:
Virgil Gligor
Carnegie Mellon University, USA
,
Moti Yung
Columbia University, USA

Copyright © 2013 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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SIGSAC: ACM Special Interest Group on Security, Audit, and Control

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New York, NY, United States

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Published: 04 November 2013

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CCS'13

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CCS'13: 2013 ACM SIGSAC Conference on Computer and Communications Security

November 4 - 8, 2013

Berlin, Germany

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CCS '13 Paper Acceptance Rate 105 of 530 submissions, 20%;

Overall Acceptance Rate 1,261 of 6,999 submissions, 18%

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CCS '25

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ACM SIGSAC Conference on Computer and Communications Security

October 13 - 17, 2025

Taipei , Taiwan

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Cited By

Zhao HSong X(2024)TOAR: Toward Resisting AS-Level Adversary Correlation Attacks Optimal Anonymous RoutingMathematics10.3390/math1223364012:23(3640)Online publication date: 21-Nov-2024
https://doi.org/10.3390/math12233640
Zhu DTu JCai DJiang TXiahou JChen YLiu C(2024)SRFACS: A secure and robust framework for anonymous communication systemsPLOS ONE10.1371/journal.pone.031281719:12(e0312817)Online publication date: 2-Dec-2024
https://doi.org/10.1371/journal.pone.0312817
Zhai JZhang KZeng XMeng YLiu G(2024)FlowCorrGCN: Enhancing Flow Correlation Through Graph Convolutional Networks and Triplet NetworksInternational Journal of Intelligent Systems10.1155/2024/88235112024:1Online publication date: 30-Oct-2024
https://doi.org/10.1155/2024/8823511
Pahl SKaiser DEngel TAdamsky FAyday EVaidya J(2024)OnionVPN: Onion Routing-Based VPN-Tunnels with Onion ServicesProceedings of the 23rd Workshop on Privacy in the Electronic Society10.1145/3689943.3695043(17-33)Online publication date: 20-Nov-2024
https://dl.acm.org/doi/10.1145/3689943.3695043
Luo ZBhat ANayak KKate A(2024)Attacking and Improving the Tor Directory Protocol2024 IEEE Symposium on Security and Privacy (SP)10.1109/SP54263.2024.00083(3221-3237)Online publication date: 19-May-2024
https://doi.org/10.1109/SP54263.2024.00083
Tan RTan QWang HXie YZhang P(2024)P-I2Prange: An Automatic Construction Architecture for Scenarios in I2P Ranges2024 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN60899.2024.10651444(1-10)Online publication date: 30-Jun-2024
https://doi.org/10.1109/IJCNN60899.2024.10651444
Meng XLiang M(2024)Node Selection-Based Anonymous Network Performance Optimization Method2024 9th International Conference on Computer and Communication Systems (ICCCS)10.1109/ICCCS61882.2024.10602902(456-461)Online publication date: 19-Apr-2024
https://doi.org/10.1109/ICCCS61882.2024.10602902
Chao DXu DGao FZhang CZhang WZhu L(2024)A Systematic Survey on Security in Anonymity Networks: Vulnerabilities, Attacks, Defenses, and FormalizationIEEE Communications Surveys & Tutorials10.1109/COMST.2024.335000626:3(1775-1829)Online publication date: Nov-2025
https://doi.org/10.1109/COMST.2024.3350006
Chhabra ASaha RKumar GKim T(2024)Navigating the Maze: Exploring Blockchain Privacy and Its Information RetrievalIEEE Access10.1109/ACCESS.2024.337085712(32089-32110)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3370857
Fressancourt AIannone LKerichard M(2024)A deeper look at Ariadne: a privacy-preserving network layer protocolAnnals of Telecommunications10.1007/s12243-024-01017-579:11-12(745-762)Online publication date: 13-Mar-2024
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