A trust based framework for both spectrum sensing and data transmission in CR-MANETs
Bibliographic record
Abstract
Distributed cooperative spectrum sensing is an effective and feasible approach to detect primary users in Cognitive Radio Mobile Ad Hoc NETworks (CR-MANETs). However, due to the dynamic and interdependent characteristics of this approach, malicious attackers can interrupt the normal spectrum sensing more easily in open environments by spectrum sensing data falsification attacks. Meanwhile, attackers can perform traditional attacks to data transmission in MANETs. Towards these complicated situations in CR-MANETs, we study a new type of attack named joint dynamic spectrum sensing and data transmission attack in this paper. We propose a trust based framework to protect both distributed cooperative spectrum sensing and data transmission. For protection of distributed cooperative spectrum sensing, a weighted-average consensus algorithm with trust is applied to degrade the impact of malicious secondary users. At the same time, data transmission in a network formed by secondary users can be protected by trust with direct and indirect observations. Simulation results show the effectiveness and performance of the proposed framework under different experimental scenarios.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".