Analysis and prediction of web proxies misbehavior
Bibliographic record
Abstract
The need for anonymity and privacy has given a rise to open web proxies that act as gateways relaying traffic between web servers and their clients, allowing users to access otherwise not accessible content. As the open web proxy ecosystem continues to grow, research studies point out the extent of content alteration on the Internet. While the previous studies focused on detection and analysis of content manipulation by proxies, we focus on the feasibility of predicting these manipulations. In this work, we present a new approach for predicting the types of content alterations that might be silently introduced by open proxies. Our approach is designed to proactively indicate changes without a need to fetch the data through a proxy first. We explore the feasibility of the approach on a website content of 1028 domains fetched through 1293 proxies. We leverage our approach to proactively and accurately identify various content manipulations with 87% - 92% accuracy. Our study reveals an important observation that the majority of proxies manipulate website content based on technical information of the website and its web server.
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 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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".