Intelligent Integrated Cross-layer Authentication for Efficient Mutual Verification in UDN with Guaranteed Security-of-Service
Bibliographic record
Abstract
While conventional cryptographic authentication methods suffer from high computation overhead and long latency in ultra-dense networks (UDNs), physical (PHY) layer authentication techniques often fail to serve as substitutions due to their unreliable performance in the dynamic environment. This paper proposes an intelligent integrated cross-layer authentication scheme by combining PHY layer method and cryptographic based authentication and key agreement (AKA) method to unify their strengths for achieving efficient mutual verification with guaranteed Security-of-Service (SoS). In the proposed scheme, an intelligent switch module is designed to select the real-time optimal authentication method based on the performance evaluation of different methods for better secure provision and communication performance. Moreover, a situation-aware attribute selection algorithm is developed to select the optimal attribute for PHY layer method to further improve the authentication reliability in the dynamic communication environment. Our results demonstrate that the proposed scheme can achieve more efficient mutual verification with guaranteed SoS than the existing schemes.
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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 | 0.002 | 0.003 |
| 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) | 0.001 | 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".