Evaluation of security algorithms that combat Byzantine failures in Cognitive Radio Networks
Bibliographic record
Abstract
Distributed Cognitive Radio Network (CRN) topology is described in IEEE 802.22 standard which is the first worldwide standard operating in TV bandwidth of wireless regional area. Spectrum sensing is conducted at distributed customer premise equipment (CPE). Cooperative sensing between CPEs is widely used to obtain more accurate spectrum sensing results. However, Byzantine failures can happen when some CPEs are attacked inside the CRN. A robust Byzantine security model can be built on MAC layer of base station (BS) by using published data fusion algorithm -Weighted Sequential Probability Ratio Test (WSPRT) to assist making a decision as to whether a licensed channel is occupied by other primary users or not. In this paper different sets of simulations are run for evaluating the performance of the WSPRT algorithm in terms of correct sensing rate, miss detection rate and number of samples comparing with other three algorithms for binary hypothesis test, namely, "And", "Or" and "Majority". Simulation results also show the different performance metrics under two types of attack patterns. From the results of simulations WSPRT is shown to have better performance than the other three algorithms.
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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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 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".