short-paper

DynaFlow: An Efficient Website Fingerprinting Defense Based on Dynamically-Adjusting Flows

Authors:

Srinivas DevadasAuthors Info & Claims

WPES'18: Proceedings of the 2018 Workshop on Privacy in the Electronic Society

Pages 109 - 113

https://doi.org/10.1145/3267323.3268960

Published: 15 January 2018 Publication History

Abstract

Website fingerprinting attacks enable a local adversary to determine which website a Tor user visits. In recent years, several researchers have proposed defenses to counter these attacks. However, these defenses have shortcomings: many do not provide formal guarantees of security, incur high latency and bandwidth overheads, and require a frequently-updated database of website traffic patterns. In this work, we introduce a new countermeasure, DynaFlow, based on dynamically-adjusting flows to protect against website fingerprinting. DynaFlow provides a similar level of security as current state-of-the-art while being over 40% more efficient. At the same time, DynaFlow does not require a pre-established database and extends protection to dynamically-generated websites.

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Cited By

Xie YFeng JHuang WZhang YSun XChen XLuo XChua TNgo CKa-Wei Lee RKumar RLauw H(2024)Contrastive Fingerprinting: A Novel Website Fingerprinting Attack over Few-shot TracesProceedings of the ACM Web Conference 202410.1145/3589334.3645575(1203-1214)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3645575
Krämer PBaier BLanderer NDiederich PGriessel AHohlfeld OBlenk AMieth MKellerer W(2024)ProFi: Scalable and Efficient Website FingerprintingIEEE Transactions on Network and Service Management10.1109/TNSM.2023.331850821:1(1271-1286)Online publication date: Feb-2024
https://doi.org/10.1109/TNSM.2023.3318508
Zhou QWang LZhu HLu TSheng V(2024)WF-Transformer: Learning Temporal Features for Accurate Anonymous Traffic Identification by Using Transformer NetworksIEEE Transactions on Information Forensics and Security10.1109/TIFS.2023.331896619(30-43)Online publication date: 2024
https://doi.org/10.1109/TIFS.2023.3318966
Show More Cited By

Index Terms

DynaFlow: An Efficient Website Fingerprinting Defense Based on Dynamically-Adjusting Flows
1. Networks
  1. Network properties
    1. Network privacy and anonymity
2. Security and privacy
  1. Security services
    1. Privacy-preserving protocols
    2. Pseudonymity, anonymity and untraceability

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Published In

cover image ACM Conferences

WPES'18: Proceedings of the 2018 Workshop on Privacy in the Electronic Society

October 2018

190 pages

ISBN:9781450359894

DOI:10.1145/3267323

General Chairs:
David Lie
University of Toronto, Canada
,
Mohammad Mannan
Concordia University, Canada
,
Program Chair:
Aaron Johnson
U.S. Naval Research Laboratory, USA

Copyright © 2018 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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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 15 January 2018

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Short-paper

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CCS '18

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SIGSAC

CCS '18: 2018 ACM SIGSAC Conference on Computer and Communications Security

October 15, 2018

Toronto, Canada

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WPES'18 Paper Acceptance Rate 11 of 25 submissions, 44%;

Overall Acceptance Rate 106 of 355 submissions, 30%

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CCS '24

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Citations

Cited By

Xie YFeng JHuang WZhang YSun XChen XLuo XChua TNgo CKa-Wei Lee RKumar RLauw H(2024)Contrastive Fingerprinting: A Novel Website Fingerprinting Attack over Few-shot TracesProceedings of the ACM Web Conference 202410.1145/3589334.3645575(1203-1214)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3645575
Krämer PBaier BLanderer NDiederich PGriessel AHohlfeld OBlenk AMieth MKellerer W(2024)ProFi: Scalable and Efficient Website FingerprintingIEEE Transactions on Network and Service Management10.1109/TNSM.2023.331850821:1(1271-1286)Online publication date: Feb-2024
https://doi.org/10.1109/TNSM.2023.3318508
Zhou QWang LZhu HLu TSheng V(2024)WF-Transformer: Learning Temporal Features for Accurate Anonymous Traffic Identification by Using Transformer NetworksIEEE Transactions on Information Forensics and Security10.1109/TIFS.2023.331896619(30-43)Online publication date: 2024
https://doi.org/10.1109/TIFS.2023.3318966
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
Hong XLi SMa XLiu BWu F(2024)A website fingerprinting technology with time-samplingPeer-to-Peer Networking and Applications10.1007/s12083-024-01646-617:2(944-960)Online publication date: 6-Feb-2024
https://doi.org/10.1007/s12083-024-01646-6
Shen MJi KGao ZLi QZhu LXu KCalandrino JTroncoso C(2023)Subverting website fingerprinting defenses with robust traffic representationProceedings of the 32nd USENIX Conference on Security Symposium10.5555/3620237.3620272(607-624)Online publication date: 9-Aug-2023
https://dl.acm.org/doi/10.5555/3620237.3620272
Wang YWang YHuang JChen Y(2023)TBP: Tree structure Burst-sequence Padding Defense Against Website Fingerprinting2023 IEEE International Performance, Computing, and Communications Conference (IPCCC)10.1109/IPCCC59175.2023.10253867(99-108)Online publication date: 17-Nov-2023
https://doi.org/10.1109/IPCCC59175.2023.10253867
Wang ZZhang YDong PZhang H(2023)PWR: Path Weighted Random Scheme in Multipath Traffic Splitting Against Website FingerprintingIEEE Access10.1109/ACCESS.2023.331536011(101273-101285)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3315360
Liu PHe LLi Z(2023)A Survey on Deep Learning for Website Fingerprinting Attacks and DefensesIEEE Access10.1109/ACCESS.2023.325355911(26033-26047)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3253559
Mohd Aminuddin MZaaba ZSamsudin AZaki FAnuar N(2023)The rise of website fingerprinting on TorJournal of Network and Computer Applications10.1016/j.jnca.2023.103582212:COnline publication date: 1-Mar-2023
https://dl.acm.org/doi/10.1016/j.jnca.2023.103582
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