Dude, where’s that IP?: circumventing measurement-based IP geolocation
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
Many applications of IP geolocation can benefit from geolocation that is robust to adversarial clients. These include applications that limit access to online content to a specific geographic region and cloud computing, where some organizations must ensure their virtual machines stay in an appropriate geographic region. This paper studies the applicability of current IP geolocation techniques against an adversary who tries to subvert the techniques into returning a forged result. We propose and evaluate attacks on both delay-based IP geolocation techniques and more advanced topology-aware techniques. Against delay-based techniques, we find that the adversary has a clear trade-off between the accuracy and the detectability of an attack. In contrast, we observe that more sophisticated topology-aware techniques actually fare worse against an adversary because they give the adversary more inputs to manipulate through their use of topology and delay information.
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.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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