Website Fingerprinting Attacks with Advanced Features on Tor Networks
Bibliographic record
Abstract
The Tor network has been discovered to be susceptible to website fingerprinting (WF) attacks. Previous research has primarily been conducted in controlled experimental environments, leading to debates about the feasibility of WF attacks in real-world environments. Recent advancements in feature engineering and machine learning models have aimed to bridge this gap by exploring real-world scenarios. Nonetheless, designing innovative features for WF attacks on Tor networks remains a significant challenge. To address these challenges, this research explores real-world environments using a novel method that extracts network traffic data by filtering out common traffic based on onion service protocols and advanced features that optimize various advantages of different features. The results suggest that WF attacks in real-world environments become more feasible with the proposed method and carefully crafted features. This study contributes to our understanding of the feasibility of WF attacks on Tor networks in real-world environments and can help identify potential security enhancements on Tor networks.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".