An Overview and Future Directions on Physical-Layer Security for Cognitive Radio Networks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Cognitive radio networks (CRNs) have emerged as a reliable technology to address the problem of spectrum under-utilization. Cognitive radio (CR) is known to be intelligent, as it can sense, learn, and change its transmitting and receiving parameters according to changes in the surrounding environment. These operations are characterized by the CR cognition cycle. During the three stages of cognition, users are vulnerable to several types of attacks on the physical layer. Various types of physical-layer attacks can lead to loss of data security over CRNs. In this article, an overview of physical-layer security (PLS) for CRNs is provided. Some of the major attacks over the physical layer of CRN are presented, such as primary user emulation attack (PUEA), sensing falsification, jamming, and eavesdropping. Moreover, some of the suggested methods to combat these attacks and ensure data privacy are provided. We focus on presenting certain methods to combat eavesdropping. Current challenges and future research directions are included.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it