RUDOLF: An Efficient and Adaptive Defense Approach Against Website Fingerprinting Attacks Based on Soft Actor-Critic Algorithm
Bibliographic record
Abstract
Although Tor is designed to provide anonymity, website fingerprinting (WF) attacks have posed significant threats to user privacy. In response, various defense approaches have been developed. Randomization and regularization-based defenses are criticized to be inefficient due to their bandwidth-consuming nature. Some adversarial learning-based defenses are impractical because the generation of perturbation depends on the complete traffic traces. Other adversarial learning-based defenses have weaknesses of lacking adaptability because their perturbations are input-agnostic. To overcome these shortcomings, we propose RUDOLF, an efficient and adaptive WF defense based on the soft actor-critic (SAC) algorithm of reinforcement learning (RL). We train the agent that can incrementally output perturbations synchronously following each burst of real-time traffic. Different from previous defenses, RUDOLF’s perturbation does not depend on the integrity of the traffic and concerns the actual real-time traffic, which ensures the practicality of implementation and adaptability. Besides, we take advantage of the exploratory characteristics of the SAC algorithm to obtain the optimal policy of adding perturbations that can efficiently balance defense effects and bandwidth consumption. Experiments on synthetic datasets show that with less than 30% bandwidth overhead (BWO), RUDOLF can reduce the average attack accuracy to around 15%–20%, which is superior to previous works. We also have implemented RUDOLF as a Tor pluggable transport. The performance in the real Tor network shows that RUDOLF can reduce the average accuracy of WF classifier to around 24% with about 25% BWO and almost no time delay.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
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 source (direct Gemma or distilled Codex), 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".