Mitigating Smart Selfish MAC Layer Misbehavior in Ad Hoc Networks
Why this work is in the frame
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Bibliographic record
Abstract
Security is a fundamental prerequisite for network survivability and reliability in mobile ad hoc networks (MANET). In the presence of selfish nodes that disobey the standard, the performance of well-behaved nodes will significantly degrade. In this paper, we focus on identifying potential threats in medium access control (MAC) layer introduced by selfish nodes, especially "smart" attack strategies that can defeat the existing detection and reaction systems against MAC layer selfish misbehavior. Furthermore, we propose predictable random backoff (PRB) algorithm that is capable of mitigating the impact of these vulnerabilities. PRB is based on minor modification of IEEE 802.11 binary exponential backoff (BEB) and forces each node to generate "predictable" random backoff intervals. Via computer simulations, we show that PRB is fairly efficient in ensuring reasonable throughput for well-behaved flows in the presence of selfish flows
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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 it