Security in physical layer of cognitive radio networks
Bibliographic record
Abstract
Fifth-generation (5G) networks and beyond are anticipated to support a vast number of connections and services. Due to the enormous amount of confidential data shared between devices in these networks, the risk of security vulnerabilities escalates proportionally. Cognitive radio networks (CRNs) are no exception since they are vulnerable to a variety of physical-layer threats; hence, a physical-layer approach is necessary to safeguard these networks. Consequently, physical-layer security (PLS) has recently been applied for examining and strengthening the security of wireless networks, including CRNs. In light of this, this chapter examines a brief overview of CRNs, as well as the main physical layer attacks and their respective primary countermeasures. In addition, the employment of energy harvesting (EH) techniques to improve CRNs security is discussed. The security threats on cognitive unmanned aerial vehicles (UAVs) and their primary defense mechanisms are presented. In addition, the consideration of cascaded fading channels and their effect on the security of CRNs are explored. Finally, this chapter includes conclusions and potential future directions.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.007 |
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".