Multilayer Authentication for Communication Systems Based on Physical-Layer Attributes
Bibliographic record
Abstract
In this paper, a multilayer security solution is introduced, in order to accord the required end-to-end security blanket to the heterogeneous networks by considering the properties used by authentication at the physical-layer in transport-layer authentication. In particular, after achieving an authentication level based on the estimated channel impulse response (CIR) at the physicallayer, these CIRs are exploited at the transport layer, adding more randomness to the generated sequence numbers used in the 3-Way TCP/IP handshake authentication. Furthermore, in order to enhance the authentication at the physical layer, the estimated CIR is quantized into two domains: amplitude and phase. The quantizer’s output is used to differentiate between the legitimate transmitters and intruders using binary hypothesis testing. Eventually, generating a unique sequence numbers is granted due to the increased randomness offered by the quantizer outputs. In order to verify the effectiveness of the proposed scheme, simulation results are shown based on an orthogonal frequency division multiplexing (OFDM) system. Additionally, a logarithmic likelihood ratio test is used to evaluate the authentication performance.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.003 |
| 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".