Intelligent Integrated Cross-layer Authentication for Efficient Mutual Verification in UDN with Guaranteed Security-of-Service
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it