Active networking approach to the design of adaptive virtual private networks
Bibliographic record
Abstract
As virtual private networks (VPNs) penetrate into the internetworking community, they face a number of challenges, such as the requirement for on-demand creation and termination of tunnels, flexible services and interface features allowing for easy integration with a wide range of applications, and extended geographical reach through dynamic installation of services. We present a novel approach to the design of an adaptive VPN framework that can offer flexible, portable services and customizable VPN mechanisms to provide on-demand secure tunnels in a dynamic environment. We base our approach on the active networking technology, which is a networking paradigm that inserts intelligence into the network by offering a dynamic programming capability to network routers. The proposed architecture implements encryption, key management and data integrity services to support VPN functions. Experimental results from our test bed provide latency and throughput measurements. We discuss four deployment scenarios that can take advantage of the adaptive VPN services: a) dynamic secure multicast trees; b) Secure item look-ups in online auction systems; c) secure code distribution; and d) secure agent traversal.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".