Fast authentication in 5G HetNet through SDN enabled weighted secure-context-information transfer
Bibliographic record
Abstract
Future fifth generation (5G) wireless infrastructure tends to be highly heterogeneous, with dense small cells deployed overlay to cellular networks. Along with extremely high capacity and stringent latency requirements, security provisioning is becoming challenging in 5G Heterogeneous Networks (HetNets). Security key management could be difficult in small cells where users join and leave frequently, not to mention the limited capability of simplified access points (APs). On the other hand, frequent handovers and authentications in small cells also introduce unnecessary latency. Therefore in this article, we propose a software defined networking (SDN) enabled fast authentication scheme using weighted secure-context-information (SCI) transfer in order to improve authentication efficiency during handover and meet 5G latency requirement. The proposed algorithm is then applied in Neyman Pearson (NP) hypothesis test to authenticate users, which shows enhanced authentication accuracy and reduced latency in MATLAB simulations. Furthermore, we first analyze the SDN structure using priority queuing theory, and prove the performance of SDN enabled authentication handover.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".