Bibliographic record
Abstract
There is currently enormous interest in the design of secure wireless networks. This has been necessitated by the fact that free-space radio transmission in wireless networks makes eavesdropping easy and consequently, a security breach may result in unauthorized access, information theft, interference, jamming and service degradation. Virtual private networks (VPN) have emerged as an important solution to security threats surrounding the use of public networks for private communications. VPN provide security by integrating a set of authentication, encryption, access control and session management components. While VPN for wireline networks have matured in both research and commercial environments, the design and deployment of wireless VPN is still an evolving field. This paper presents the results of an ongoing sub-project within the Secure Active VPN Environment (SAVE) project at Dalhousie University. The primary objective of this paper is to present the design and implementation of a secure wireless LAN based on the IPSec VPN tunnelling protocol and investigate its performance. An IPSec-compliant VPN is constructed and the traffic between the wireless node and the gateway is protected by the IPSec tunnel. PGP certification is used to provide secure public key management. UDP and TCP performance analysis are done to determine the effects of IPSec service on the wireless VPN. A further TCP trace analysis is done to determine the pipe capacity usage on the wireless VPN.
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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".