ReDiSen: Reputation-based secure cooperative sensing in distributed cognitive radio networks
Bibliographic record
Abstract
Cognitive radio techniques represent an emerging approach for mitigating the spectrum scarcity problem in wireless communications. Cooperative sensing is an effective solution to improve sensing accuracy and robustness in the presence of fading and shadowing that make individual sensing less reliable. However, when an adversary can corrupt some nodes in the network, the effectiveness of cooperative sensing may degrade dramatically. We design the first fully distributed security scheme ReDiSen to counter attacks in cooperative sensing. We apply reputation generated from exchanged sensing results as an aid to restrict the impact of the malicious behaviours. Both theoretical analysis and simulation results indicate that ReDiSen provides an effective countermeasure against security attacks by enabling secondary users to obtain more accurate cooperative sensing results in adversarial environments. ReDiSen does not rely on a central authority, nor a common control channel, and is therefore more applicable in dynamic cognitive radio networks.
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How this classification was reachedexpand
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 | 0.001 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".