Classifying Poisoning Attacks in Software Defined Networking
Bibliographic record
Abstract
Software-Defined Networking (SDN) provides significant flexibility when it comes to complex network management. This makes this technology an ideal candidate for dealing with network management issues in satellite and terrestrial networks. One key innovation of SDN is the separation of the control plane from the data plane. This results in a new network element: the controller. Given the importance of the role of the logically centralised (physically distributed) controller, it becomes an important point to protect in the new SDN paradigm. It could be vulnerable to attacks that are common in traditional networks such as Distributed Denial of Service (DDoS). In this paper, we address a type of attack that could threaten the operation of SDN-based environments: poisoning attacks. To perform its function, the logically centralised controller must have an accurate view of the network state. The accuracy of this view is crucial to the operation of the network. This view is obtained by exchanging information among controllers and between controllers and network elements. Such information flow could be vulnerable to different types of poisoning attacks. The motivation for writing this paper is that (1) poisoning attacks on SDN networks could have great impact, (2) most of them are relatively recent and (3) the differences between such attacks could be subtle. Therefore, we address the issues by classifying poisoning attacks in SDN. We classify both attacks and defences. For attacks we make a distinction between direct poisoning attacks and attacks that are designed to evade a specific defence.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".