A Security-Mode for Carrier-Grade SDN Controllers
Bibliographic record
Abstract
Management approaches to modern networks are increasingly influenced by software-defined networks (SDNs), and this increased influence is reflected in the growth of commercially available innovative SDN-based switches, controllers and applications. To date, there have been a number of commercial and open-source SDN operating systems (NOS) introduced for various purposes, including distributed controller frameworks targeting large, carrier-grade networks such as the Open Network Operating System (ONOS) and OpenDayLight (ODL). These frameworks are distinguished by their (i) elastic cluster controller architecture, (ii) network virtualization support, and (iii) modular design. Given their flexible design, growing list of supported features, and collaborative community support, these are attractive hosting platforms for a wide range of third-party distributed network management applications. This paper identifies the common security requirements for policy enforcement in such distributed controller environments. We present the design of a network application permission-enforcement model and an integrated security subsystem (SM-ONOS) for managing distributed applications running on an ONOS controller. We discuss the underlying motivations of its security extensions and their implications for improving our understanding of how to securely manage large-scale SDNs. Our performance assessments demonstrate that the security-mode extension imposed reasonable overheads (ranging from 5 to 20% for 1-7 node clusters).
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| 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".