Covert Communication Based on Non-Ideal Detection of Overt Channels
Bibliographic record
Abstract
In this paper, we study a covert communication strategy based on non-ideal detection on overt channels in Internet of Things (IoT) networks, where IoT device utilizes existing overt channels as spectrum masks to achieve covert communication. We consider the non-ideal detection of IoT device on overt channels. At the same time, to improve covert transmission rate, we use improper Gaussian signaling (IGS) at IoT device. We first provide detection errors of IoT device, and then analyze the non-ideal transmission rate using IGS and minimum error detection probability of the warden. Next, we jointly optimize the transmit power of IoT device and circularity coefficient of IGS to maximize the covert rate while meeting the quality of service (QoS) constraint of the overt channel and the covertness constraint. The final simulations demonstrate that considering the non-ideal detection of IoT device and utilizing IGS can effectively improve the system transmission rate.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| 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".