Location Verification of Wireless Internet Clients: Evaluation and Improvements
Bibliographic record
Abstract
Client Presence Verification (CPV) was proposed in previous literature as a delay-based location verification algorithm that iteratively estimates Internet delays to corroborate assertions about a client's geographic presence in a prescribed region, e.g., before granting access to a location-based service. We evaluate CPV's performance in the presence of clients that use 802.11 networks by analyzing the following factors: the number of such clients in the network, how far adversaries are from their true locations, and the number of CPV iterations required to neutralize the effect of wireless networks. We use a mix of real-world traffic measurements from PlanetLab and existing wireless-delay probability models to create the evaluation datasets. The results indicate that, while wireless delays affect CPV's performance (e.g., from 3 to ~4.7 percent false reject/accept rates), CPV can mitigate the impact of such delays by performing more delay measurements prior to location verification. This work highlights the importance of including mitigation capabilities while designing security-sensitive applications and protocols to deal with the effect of wireless delays. This will become increasingly important with the ubiquitous use of mobile devices that is expected to increase with the introduction of new computing and communication paradigms such as the Internet of Things.
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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 | 0.008 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.004 | 0.003 |
| 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".