A Proxy Identifier Based on Patterns in Traffic Flows
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Proxies are used commonly on today's Internet. On one hand, end users can choose to use proxies for hiding their identities for privacy reasons. On the other hand, ubiquitous systems can use it for intercepting the traffic for purposes such as caching. In addition, attackers can use such technologies to anonymize their malicious behaviours and hide their identities. Identification of such behaviours is important for defense applications since it can facilitate the assessment of security threats. The objective of this paper is to identify proxy traffic as seen in a traffic log file without any access to the proxy server or the clients behind it. To achieve this: (i) we employ a mixture of log files to represent real-life proxy behavior, and (ii) we design and develop a data driven machine learning based approach to provide recommendations for the automatic identification of such behaviours. Our results show that we are able to achieve our objective with a promising performance even though the problem is very challenging.
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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 | 0.001 | 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 | 0.000 | 0.000 |
| Open science | 0.001 | 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 it