SMART: Shared Memory based SDN Architecture to Resist DDoS ATtacks
Bibliographic record
Abstract
Software-Defined Networking (SDN) is a virtualised yet promising technology that is gaining attention from \nboth academia and industry. On the one hand, the use of a centralised SDN controller provides dynamic \nconfiguration and management in an efficient manner; but on the other hand, it raises several concerns mainly \nrelated to scalability and availability. Unfortunately, a centralised SDN controller may be a Single Point Of \nFailure (SPOF), thus making SDN architectures vulnerable to Distributed Denial of Service (DDoS) attacks. \nIn this paper, we design SMART, a scalable SDN architecture that aims at reducing the risk imposed by the \ncentralised aspects in typical SDN deployments. SMART supports a decentralised control plane where the \ncoordination between switches and controllers is provided using Tuple Spaces. SMART ensures a dynamic \nmapping between SDN switches and controllers without any need to execute complex migration techniques \nrequired in typical load balancing approaches.
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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 | 0.001 | 0.001 |
| 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 | 0.002 | 0.002 |
| Research integrity | 0.001 | 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".