Probabilistic Evidence Aggregation for Malicious Node Position Bounding in Wireless Networks
Why this work is in the frame
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Bibliographic record
Abstract
Abstract — Hyperbolic position bounding of malicious devices aims to estimate the location of a wireless network rogue insider that transmits an attack message containing falsified information to mislead honest nodes. A probabilistic path loss model is used to construct an area in Euclidian space bounded by minimum and maximum distance difference hyperbolas between each pair of trusted receivers. This hyperbolic area is said to contain the rogue insider with a degree of confidence. We explore the combination of evidence provided by a set of multiple receiver pairs supporting the intersection of their hyperbolic space. We propose a novel heuristic scheme to aggregate area probability so that the combined degree of confidence ascribed to the intersecting space is computed according to a paradigm of supportive rather than competitive evidence. Performance evaluation concludes that our aggregation model yields a probability distribution better fitted to experimental location estimation results than a redistributive paradigm.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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