On the Impact of Flooding Attacks on 5G Slicing with Different VNF Sharing Configurations
Bibliographic record
Abstract
Virtualized Network Function (VNF) sharing among multiple Fifth Generation (5G) slices allows network operators to increase the efficiency and utilization of the network. However, this sharing of VNFs can result in a degradation of the performance of the slices in the presence of an attack. In this paper, we evaluate the impact of flooding attacks on the performance of 5G slices with different VNF sharing configurations. We consider two VNF sharing configurations, in the first configuration, the Session Management Function (SMF) and User Plane Function (UPF) are shared among the deployed slices, while the SMF and UPF of the slices are isolated in the second configuration. The performance of these configurations is evaluated using different traffic types under two flooding attack scenarios; a ping flood attack targeting the data plane of 5G network, and a registration request flood attack directed at the control plane of 5G network. Our results showed different responses in the control and data planes. In the data plane, isolating VNFs of the slices provides better performance and mitigates the adverse effects of the attacks studied.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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 teacher head, 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".