Malicious users control and management in cognitive radio networks with priority queues
Bibliographic record
Abstract
Malicious users (MUs) have the tendency to disrupt the activities of honest users in the network if not properly controlled. In a massive cognitive radio network (CRN) with priority queues, malicious secondary users (SUs) can manipulate their priority queue requirements and mislead legitimate SUs to vacate the channels. In this paper, a game theoretic based signal detection approach is proposed to control the presence of MUs in CRN. If the received signal strength is less than the predefined threshold for primary transmissions in the presence of interference and noise, such a user is marked to be malicious and its payoff table is updated. Through the mixed strategy Nash equilibrium method, the payoff table of each user can be updated to aid removal of MUs from the network. The outcome of the simulation results shows that such an approach can reduce the impact of malicious activities in the massive CRN where SUs are expected to be low-power energy-efficient devices.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".