Constructing secured cognitive wireless networks: experiences and challenges
Bibliographic record
Abstract
Abstract Recent development in wireless communications has lead to the problem of growing spectrum shortage. Cognitive radio, as a novel technology, has been proposed in recent years to solve this problem by dynamically accessing the spectrum so as to enhance the spectrum utilization. Security in cognitive radio networks has become a challenging issue because there are more chances open to attackers by cognitive radio technology, compared to those from conventional wireless networks. These weaknesses and vulnerable aspects, introduced by the nature of cognitive radio, may cause serious impact on the security and quality of service (QoS) provisioning for the entire network. Unfortunately, at present, there is no specific secure protocol designed for cognitive radio networks. Therefore, in this paper, we conduct a high‐level survey to review and reflect the state‐of‐the‐art work on the security issues in cognitive networks. We focus on analyzing the security system at the macroscopic level, where both protection and detection are considered to be the most essential parts to ensure security in the whole network. Furthermore, we investigate special characteristics of cognitive networks at different protocol layers, including physical layer, link layer, network layer, transport layer and application layer. We recognize both the challenges and experiences, and focus on constructing the secured cognitive wireless networks. Copyright © 2009 John Wiley & Sons, Ltd.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 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".