Jamming Strategies in DYMO MANETs: Assessing Detectability and Operational Impacts
Bibliographic record
Abstract
The wide deployment of smart phones has begun to make them pragmatic deployment environments for real-world at-scale MANETs (i.e., to provide peer-to-peer based non-cellular services). Such services, of course, will be subject to cyber-attacks, one of the simplest of which is radio frequency (RF) jamming. The success of these MANETs will require both: i) robust and accurate methods of detecting when jammers are present and ii) methods of mitigating jammer impacts. This work explores the effects that various jamming strategies have on MANET operations as observed via standard MANET operational measures such as: packet delivery ratio, delay, routing overhead, and hops travelled. It is shown that the detect ability of active jammers heavily depends both on which measure is used and the exact nature of the jamming strategy employed. Moreover, although basic approaches such as constant jamming are easily detectable, it is shown that little work is required to construct far less detectable jamming strategies.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".