Evaluation of Complex Surveillance Systems for Emergent Vulnerability
Bibliographic record
Abstract
The current paradigm for testing tracking and surveillance systems is to identify representative metrics for system components, then optimize the performance of that metric against test data. The assumption is that optimization of individual components will optimize the surveillance system as a whole. However, while optimizing components is a necessary step to improve systems, it is not sufficient to address vulnerabilities that emerge in a large system with many components. A large surveillance system will have many cameras and other sensors. In some cases, to cover more area, the cameras and sensors may be mobile. Coverage is unlikely to be complete in all areas at all times, so sensor allocation will follow some policy. The combination of sensors, sensor properties, mobility and policy can result in a system that is vulnerable in ways that are difficult to predict. We present a method to model and predict emergent vulnerabilities in a complex surveillance system. To demonstrate the method, we apply it to a downscaled physical surveillance system that uses multiple stationary and mobile camera platforms to monitor and defend against intrusions. Our method finds two vulnerabilities in the system in simulation, one of which we demonstrate with the physical system.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".