On Estimating the Topology of an Adversarial Wireless Network
Why this work is in the frame
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Bibliographic record
Abstract
Owing to recent interest in sensor networks for military and security applications, studies of the security vulnerabilities of these networks are becoming increasingly important. In the present paper, the problem of estimating the topology of an adversarial sensor network is considered. The adversarial network is assumed to employ strong encryption, so that its transmitted packets are assumed to be unreadable by the observer. Thus, the algorithms are required to make use of the time correlations in channel uses by the adversarial network. Assuming the use of the MACA protocol, our algorithms are capable of estimating both the routes used by nodes in the adversarial sensor network, and as the identities of the nodes that are within each other's neighborhood (i.e., within radio range of each other), so that an attack could be designed for maximum effect. Results are presented which show that route estimation can be accomplished quickly using our algorithm, and that neighborhood estimation can be accomplished in a reasonable amount of time.
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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 | 0.000 | 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 | 0.000 |
| 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