A realistic implementation for simulating side-channel in mobile ad hoc networks
Bibliographic record
Abstract
In this paper, we present the necessary methods and techniques for simulating a wide-band multi-hop side-channel between two distant peer nodes in a Mobile Ad hoc Network (MANET). Simulating such a side-channel is helpful in understanding its potential and experimenting with its cyber warfare benefits, and for discovering effective ways to detect it. Implementing a content-bearing side-channel in which the full frame payload is used for messaging requires the ability to access the full communication stack in the simulator. We have implemented a fully functional multi-hop side-channel on the EXata/Cyber (QualNet) simulation tool to a level of detail where it could potentially be used in-line with applications such as voice or video for real-time, real-life emulations. We provide the details of our implementation and evidence of the benefits of such a side-channel via test scenarios. Such simulations may be used to facilitate military personnel's understanding of the effects cyber tools may have on their operations, in particular, Adaptive Dispersed Operations where military units are mobile.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".