Mitigating Smart Selfish MAC Layer Misbehavior in Ad Hoc Networks
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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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 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".