Security impacts on establishing MPLS/BGP VPNs
Bibliographic record
Abstract
Abstract Multi‐protocol label switching (MPLS) is considered as the future routing technology of the Internet. Service providers with MPLS‐enabled core infrastructure benefits from the capabilities of this promising protocol to offer incremental value‐added services to their end clients. Virtual private network (VPN) is among many of the services provided by MPLS. Security is not guaranteed with VPN implementation, but it is implied, that is, the users expect to receive a secure connection. Two security concerns of importance for VPNs are customer edge (CE) and provider edge (PE) security. The customer edge is the connection from the customer site to the provider's site. PE is the connection between two providers' site. In this paper, we describe testbed experiences and procedures to study security issues in provider edge MPLS/BGP VPN networks. First, we investigate security constraints in configuring a BGP/MPLS VPNs where the provider's core transport infrastructure supports MPLS. Secondly, we consider the use of GRE tunnel with IPsec in the case where no MPLS support exists in provider's infrastructure. We present the performance results on establishing a secure VPN between two PEs in terms of protocol packet overhead and latency. Copyright © 2008 John Wiley & Sons, Ltd.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".