TLS Performance Evaluation in the Control Plane of a 5G Core Network Slice
Bibliographic record
Abstract
3GPP has recommended 5G service providers adopt Transport Layer Security (TLS) to secure communications among different Network Functions (NFs) in 5G core network slice. It is imperative to study the impact of TLS on 5G control plane traffic. Previous research has shown that TLS adds costs to protocols, like Session Independent Protocol (SIP) and The Onion Router (ToR). This study aims to study different TLS cipher suites impact on network performance parameters such as packet size, time, and the number of messages sent in distinct 5G control flows. We emulated a 5G core network slice using an open-source project and measured the overhead on the network parameters while 5G slice components transmit control plane traffic in both cases of TLS and no TLS. We examined the impact of three cipher suites of TLS with key exchange algorithm of Rivest Shamir Adleman (RSA) on the NF registration with Network Repository Function (NRF).
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".