Covert Surveillance via Proactive Eavesdropping Under Channel Uncertainty
Bibliographic record
Abstract
Surveillance performance is studied for a wireless eavesdropping system, where a full-duplex legitimate monitor eavesdrops a suspicious user's link with artificial noise (AN) assistance. Different from the existing works, the suspicious receiver is assumed to be capable of detecting the presence of AN. Once such receiver detects the AN, the suspicious user will stop transmission, which can therefore degrade the surveillance performance. Hence, to improve the surveillance performance, AN should be transmitted covertly with a low detection probability. Under these assumptions, an optimization problem is formulated to maximize the surveillance performance under a covert constraint. Then, based on the detection ability at the suspicious receiver, a novel scheme is proposed to solve the optimization problem using an iterative search. Moreover, we investigate the impact of both the suspicious-transmitter-to-suspicious-receiver and the monitor-to-suspicious-receiver links uncertainties on the covert surveillance performance. Simulations are performed to verify the analyses. We show that the uncertainty in the suspicious user's link can enhance the surveillance performance, while the uncertainty in the monitor-to-suspicious-receiver link can degrade the surveillance performance.
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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 | 0.001 | 0.006 |
| 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 | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".