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Retraction Notice: Website Fingerprinting Defensive Algorithms Against Various Attacks

2023· article· en· W4403061671 on OpenAlexaff
M. Nirmala, B Rajalakshmi, Pragya Goel, Swapnil Shukla, Pragati Subhadarshinee

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicInternet Traffic Analysis and Secure E-voting
Canadian institutionsHorizon College and Seminary
Fundersnot available
KeywordsComputer scienceComputer securityAlgorithmInternet privacy

Abstract

fetched live from OpenAlex

A local listener can identify the websites a user is accessing via an encrypted connection thanks to website fingerprinting. Modern web fingerprinting techniques have been demonstrated to be successful even against Tor. RBP and Walkie-Talkie, two recent lightweight website fingerprinting protections for Tor that significantly weaken current attacks, have been suggested. We attempted to compare the current Website fingerprinting attack strategies and defense algorithms in this paper. RBP and Walkie-Talkie algorithms have been used as protection mechanisms in some of our studies. Our research is based on determining the efficacy of defense algorithms against different attacks and contrasting the various attack algorithms that are now accessible. To do this, we used homology analysis.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.884
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.017
GPT teacher head0.260
Teacher spread0.243 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations2
Published2023
Admission routes1
Has abstractyes

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