An enhanced security scheme for query state inference in EPCglobal discovery services
Bibliographic record
Abstract
The EPCglobal Network is a global network that provides trade partners with real-time and accurate data sharing capabilities. Discovery Services refer to a suite of network services performing the critical lookup function, which consists of querying a common repository in order to localize all information sources with relevant data. Given the high sensitivity of the exchanged data in the EPCglobal Network, it becomes crucial to secure Discovery Services. In this paper, we propose a probabilistic security scheme capable of detecting suspicious Discovery Services queries during transaction. The query is first converted into a vector of observed real values. These observed values are assumed to follow a Gaussian distribution both for safe and suspicious queries. Then, a classification algorithm computes a score for each state; i.e. safe and suspicious, and infers the state of the query. We conducted extensive experiments. The results show that, compared to a simple Gaussian model, our proposed scheme improves both the detection rate and the false alarm rate.
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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 | 0.000 | 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 | 0.002 |
| 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 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".