Security in<scp>5G</scp>and beyond recent advances and future challenges
Bibliographic record
Abstract
Abstract 5G, 6G, and beyond (xG) technologies aim at delivering emerging services with new requirements and challenges, enabling full and hyper mobile connectivity over the world. These beyond 5G networks are expected to ensure better quality of service, very high data rate, improved network security, high capacity, low latency, and low cost. In order to meet these objectives, a number of key enabler technologies have been proposed including massive multiple input multiple output, small cells, mobile edge computing, software defined network, network function virtualization, heterogeneous networks, network slicing, cloud radio access network, ultra‐dense network, energy efficiency, and spectrum sharing. Although, the potential interest of these technologies in the network, they opened the door to many security concerns and challenges making the network security one of the primary concerns of the future wireless communication networks. In this article, we investigated the recent advancements on the xG security issues resulted by each key enabler technologies. We analyzed how to secure the network while meeting the emerging promising services, users' demands, and service requirements. We also discussed how the security issues raised by these emerging technologies can be mitigated for efficient and secure communication.
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".