Network intrusion detection and prevention middlebox management in SDN
Bibliographic record
Abstract
In traditional networks, it is difficult to manage the distributed detection and prevention nodes of IDS and IPS due to the laborious manual deployment and independent configuration. Software defined networking (SDN) provides a flexible approach to control the underlying network infrastructures efficiently. However, the OpenFlow flow table is too simple to provide complex functions with the match-action style processing. To support more functionalities, in this paper, we propose a middlebox management architecture with SDN - OpenMiddlebox, by extending OpenFlow to support middleboxes with ClickOS virtual machines (VM), so that programmable middleboxes could be deployed and managed in switches with fast booted ClickOS VMs flexibly. We then design automatic deployment and update schemes of network intrusion detection and prevention middleboxes with the centralized controller. The evaluation results show that OpenMiddlebox could manage the distributed middleboxes efficiently and is scalable to large networks, and the centralized control also improves the network intrusion detection and prevention accuracy.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".