Non-Cryptography Authentication for Wireless Communications Based on Two Uncorrelated Attributes
Bibliographic record
Abstract
Recently in modern wireless systems, the physical-layer authentication has been proved to be a viable technique that can be combined with the higherlayer cryptographic to enhance the communication security. The existing physical-layer authentication schemes are single-variable based to verify transmitter's identity. In fact, these kind of techniques are not quite reliable as the obtained information based on one characteristic is relatively more vulnerable to various interferences. Herein, a novel two-variable cross-layer authentication scheme is proposed. In particular, the proposed scheme provides a novel solution to detect spoofing attacks based on the time of arrival (TOA) as well as the received channel power indicator (RCPI). The TOA and RCPI are considered as two featured sample variables. Consequently, the legality of the both variables are also validated and a decision rule is provided to accurately authenticate the transmitter's identity according to the two observed characteristics' information. Eventually, the proposed scheme shows an improved spoofing detection capability than the single-variable authentication can provide for the IEEE 802.11 WLANs. It is noteworthy that the proposed technique can be applied to any other wireless system standards.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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 | 0.002 |
| 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".