On the Necessary Conditions for Covert Channel Existence: A State-of-the-Art Survey
Bibliographic record
Abstract
With the ability to leak confidential information in a secret manner, covert channels pose a significant threat to the confidentiality of a system. Due to this threat, the identification of covert channel existence has become an important part of the evaluation of secure systems. In this paper, we present a state-of-the-art survey discussing the conditions for covert channel existence found in the literature and we point to their inadequacy. We also examine how conditions for covert channel existence are handled by information theory. We propose a set of necessary and verifiable conditions for covert channel existence in systems of communicating agents. We aim to provide an improved understanding of covert channel communication and to build a foundation for developing effective and efficient mechanisms for mitigating covert channels in systems of communicating agents at the early stages of software development
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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 | 0.010 | 0.054 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.007 | 0.021 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".