Spoofing-Resilient Network Traffic Classification in Programmable Data Plane
Bibliographic record
Abstract
Programmable data planes facilitate near line-rate network traffic classification by maintaining per-flow statistics directly within the data plane memory. However, this capability introduces a significant vulnerability: spoofing-based attacks can inject substantial volumes of dummy flows, swiftly depleting limited memory resources and overwriting legitimate flow records. Consequently, this degrades the classification performance and disrupts the reliable traffic analysis. In this paper, we address the challenge of legitimate flow overwrites caused by spoofed flow explosions and propose a resilient in-network detection framework that safeguards data-plane resources without compromising inference accuracy. Our approach employs a data-driven method to identify spoofing behavior based on traffic characteristics and dynamically filters spoof-like sources using early-stage flow differentiation parameters. The control plane learns threshold values for these parameters from observed data and deploys them in the data plane to validate suspicious flows before allocating memory, thereby limiting unnecessary register usage by attack-like traffic. We prototype our solution in a P4-enabled software switch and demonstrate its effectiveness in mitigating memory exhaustion attacks, preserving classification reliability, and maintaining in-network inference performance under high-volume spoofing attacks.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".