A Lightweight Path Validation Scheme in Software-Defined Networks
Bibliographic record
Abstract
Software-Defined Networks (SDN) revolutionize traditional networks by separating control and data planes for enhanced agility and programmability. This separation, however, also opens up vulnerabilities, allowing adversaries to manipulate data plane forwarding and breach security policies. To counter this, we propose a Lightweight Path Validation Scheme (L-PVS) specifically designed for SDN environments. Our approach uses a simple validation scheme for packet forwarding paths that verifies the paths traversed by packets. Then, we further amplify the scheme with a network flow path validation to boost the validation efficiency. To reduce storage demands on switches during flow path validation, we develop a storage optimization method that aligns switch storage overhead with network flows rather than individual packets. Furthermore, we formulate a path partition scheme and present a Greedy-based KeySwitch Node Selection Algorithm (GKSS) to pinpoint optimal switches for path partition, significantly reducing overall data plane storage usage. Lastly, we design a technique using temporary KeySwitch nodes to identify anomaly switches when the controller encounters path validation failure. Evaluation results verify that L-PVS facilitates path validation with a reduced validation header size while minimizing the impact on processing delay and switch storage overhead.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".