Channel-based physical layer authentication
Bibliographic record
Abstract
In this paper, we study channel-based authentication, where the receiver can identify and authenticate the senders through channel vectors estimated from their frames. The authentication process is formulated as a sequence of hypothesis test problems. In order to improve the detection probability and reduce the false alarm probability, two schemes are proposed based on different classification algorithms in machine learning. Specifically, support vector machine (SVM) based authentication schemes and the linear Fisher discriminant analysis (LFDA) based authentication scheme are proposed by exploiting three channel features, including the time-of-arrivals, received signal strengths, and cyclic-features of the channels. In SVM based schemes, the linear and nonlinear SVMs are used to generate classifiers to solve the hypothesis test problems. In LFDA based scheme, a linear combination of these three channel features is used as the test statistic, which is compared with a threshold to perform authentication. Simulation results demonstrate that the proposed schemes perform better in terms of the misdetection probability and the false alarm probability than several existing typical channel-based authentication schemes. Moreover, the time complexity and space complexity of the proposed schemes are analyzed, and the LFDA based scheme performs the best.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".