Achieving Lightweight Path Validation and Packet Modification Detection in Software-Defined Networks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Software-Defined Networks (SDN) bring unprecedented agility and programmability to traditional networks by decoupling the control plane and data plane. However, this separation enables adversaries to manipulate data plane forwarding behaviors or modify packet payloads, thereby violating the network security policies set by the control plane and leading to information leakage, network congestion, or even network collapse. In this article, we propose an Enhanced Lightweight Path Validation Scheme (EL-PVS) for the SDN environment. Firstly, we propose a packet forwarding path validation scheme that verifies the paths traversed by packets, alongside a theoretical analysis of this validation process. Then, we extend the scheme with a network flow-level path validation to improve the validation efficiency, and present a storage optimization method to reduce the storage overhead in the validation process. To support large-scale deployment, we design 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 and the total number of paths requiring validation. In addition, we extend our path validation scheme to detect packet payload modification, where a multi-phase packet modification detection approach is designed, and then the detection results are integrated with path validation information to minimize switch-to-controller bandwidth usage. Finally, we present an anomaly switch identification technique to identify abnormal switches when the controller encounters validation failure. The evaluation results verify that EL-PVS enables flow-level path validation and packet modification detection with small validation header, minimizing processing delay and switch storage overhead.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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 it