Turbo covert channel: An iterative framework for covert communication over data networks
Bibliographic record
Abstract
Inspired by the challenges of designing a robust, and undetectable covert channel, in this paper we introduce a design methodology for timing covert channels that achieve provable polynomial-time undetectability. This means that the covert channel can not be detected by any polynomial-time statistical test that analyzes the samples of the covert traffic and the legitimate traffic. The proposed framework is based on modeling the covert channel as a differential communication channel, and the formulation for modulation/demodulation processes that are derived according to the communication model. The proposed scheme incorporates a trellis structure in modulating the covert message. The trellis structure is also used at the covert receiver to perform iterative demodulation/decoding of the covert message that significantly enhances the channel reliability. In addition, the paper presents an adaptive modulation strategy that improves the channel robustness without compromising the stealthiness of the channel. The combination of the adaptive modulation and the trellis structure gives the covert channel considerable flexibility and low error rate at the covert receiver. In fact, performance analysis of the channel reveals that the proposed covert communication scheme withstands extremely high levels of network noise and adversarial disruption, while it maintains an outstanding undetectability level and covert 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 | 0.003 | 0.008 |
| 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 | 0.003 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".