DFD: Adversarial Learning-based Approach to Defend Against Website Fingerprinting

Ahmed Abusnaina, Rhongho Jang, Aminollah Khormali, Dae Hun Nyang, David Mohaisen

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

17 Scopus citations

Abstract

The Onion Router (Tor) is designed to support an anonymous communication through end-to-end encryption. To prevent vulnerability of side channel attacks (e.g. website fingerprinting), dummy packet injection modules have been embedded in Tor to conceal trace patterns that are associated with the individual websites. However, recent study shows that current Website Fingerprinting (WF) defenses still generate patterns that may be captured and recognized by the deep learning technology. In this paper, we conduct in-depth analyses of two state-of-the-art WF defense approaches. Then, based on our new observations and insights, we propose a novel defense mechanism using a per-burst injection technique, called Deep Fingerprinting Defender (DFD), against deep learning-based WF attacks. The DFD has two operation modes, one-way and two-way injection. DFD is designed to break the inherent patterns preserved in Tor user's traces by carefully injecting dummy packets within every burst. We conducted extensive experiments to evaluate the performance of DFD over both closed-world and open-world settings. Our results demonstrate that these two configurations can successfully break the Tor network traffic pattern and achieve a high evasion rate of 86.02% over one-way client-side injection rate of 100%, a promising improvement in comparison with state-of-the-art adversarial trace's evasion rate of 60%. Moreover, DFD outperforms the state-of-the-art alternatives by requiring lower bandwidth overhead; 14.26% using client-side injection.

Original languageEnglish
Title of host publicationINFOCOM 2020 - IEEE Conference on Computer Communications
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2459-2468
Number of pages10
ISBN (Electronic)9781728164120
DOIs
StatePublished - Jul 2020
Event38th IEEE Conference on Computer Communications, INFOCOM 2020 - Toronto, Canada
Duration: 6 Jul 20209 Jul 2020

Publication series

NameProceedings - IEEE INFOCOM
Volume2020-July
ISSN (Print)0743-166X

Conference

Conference38th IEEE Conference on Computer Communications, INFOCOM 2020
Country/TerritoryCanada
CityToronto
Period6/07/209/07/20

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