On Characterizing and Measuring Out-of-Band Covert Channels
Bibliographic record
Abstract
A methodology for characterizing and measuring out-of-band covert channels (OOB-CCs) is proposed and used to evaluate covert-acoustic channels (i.e., covert channels established using speakers and microphones). OOB-CCs are low-probability of detection/low-probability of interception channels established using commodity devices that are not traditionally used for communication (e.g., speaker and microphone, display and FM radio, etc.). To date, OOB-CCs have been declared "covert" if the signals used to establish these channels could not be perceived by a human adversary. This work examines OOB-CCs from the perspective of a passive adversary and argues that a different methodology is required in order to effectively assess OOB-CCs. Traditional communication systems are measured by their capacity and bit error rate; while important parameters, they do not capture the key measures of OOB-CCs: namely, the probability of an adversary detecting the channel and the amount of data that two covertly communicating parties can exchange without being detected. As a result, the adoption of the measure steganographic capacity is proposed and used to measure the amount of data (in bits) that can be transferred through an OOB-CC before a passive adversary's probability of detecting the channel reaches a given threshold. The theoretical steganographic capacity for discrete memoryless channels as well as additive white Gaussian noise channels is calculated in this paper and a case study is performed to measure the steganographic capacity of OOB covert-acoustic channels, when a passive adversary uses an energy detector to detect the covert communication. The case study reveals the conditions under which the covertly communicating parties can achieve perfect steganography (i.e., conditions under which data can be communicated without risk of detection).
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 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".