Security enhancements for a user-controlled lightpath provisioning system
Bibliographic record
Abstract
User owned and managed optical networks offer new benefits compared to carrier networks. There are basically two types of user owned and managed optical networks: metro dark fibre networks and long-haul wavelength networks. A usercontrolled lightpath provisioning system is designed to address the network management challenges, where only the customer has complete visibility of its own network and no provider can see all the network elements. The prototyped management software has a service-oriented architecture and uses the Jini and JavaSpaces technologies. Within one management system for a federation, there are six key components: a Jini Lookup Service, an instance of JavaSpaces for storage of Light Path Objects (LPOs), a Jini Service Access Point (SAP), an LPO service, an instance of switch communication service for each switch in the transport layer and a Grid SAP. Since the new management system is a distributed system and the new management system may be deployed over a public Internet infrastructure, secure access to the management modules is required. The application of existing system security technologies to the new management system is analyzed. To securely transfer objects across a network, SSL is used to encrypt RMI data streams and thus data streams between Jini services. To securely execute a dynamically downloaded Java class, Jini adopts the Java security model. To securely use a dynamically downloaded proxy to communicate to a remote service, Jini Extensible Remote Invocation is implemented to support security features such as invocation constraints, remote method control, and the trust verification model.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".