Achieving Efficient Multipath Validation in Software-Defined Networks
Bibliographic record
Abstract
The programmability of Software-Defined Networks (SDN) enables multipath routing through dynamic adjustments and optimizations of network resources. However, a compromised switch can violate packet forwarding rules, creating serious security vulnerability. While path validation ensures packets follow designated paths, mainstream methods impose excessive computational burden on the controller and significant storage overhead on switches due to the uncertainty and potentially large number of packet forwarding paths. To address these issues, we propose a Naive Packet-level MultiPath Validation Scheme (NPM-PVS) as the first attempt to verify multiple forwarding paths in SDN. Building on NPM-PVS, we introduce an Enhanced Packet-level MultiPath Validation Scheme (EPM-PVS), which uses a Supplementary Validation Information (SVI) generation method to reduce the controller's load by ensuring consistent validation for packets of a network flow across various forwarding paths. To further improve the efficiency of EPM-PVS, we propose a Flow-level MultiPath Validation Scheme (FM-PVS) and implement a validation information compression method to minimize data plane storage overhead. Additionally, we introduce an anomaly switch identification method to locate compromised switches when path validation fails at the controller. Evaluation results demonstrate that the proposed FM-PVS achieves low switch storage overhead and reduces the computational burden on the controller.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".