An Intelligent Traffic Classification in SDN-IoT: A Machine Learning Approach
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
In recent years, there has been a sharp increase in IoT devices. Majority of these IoT devices have strict QoS requirements. This has made it very difficult for network providers to provide good network solutions whiles keeping cost in check. To meet the QoS demands in IoT networks, a new paradigm, SDN-IoT, leveraging the advantages of SDN architecture on IoT networks have been proposed to improve network quality. The programmability of the SDN controller allows the application of Machine learning in networks. This paper proposes a Machine learning model that classifies traffic in SDN-IoT networks for traffic engineering. The classification process compares the random forest algorithm, decision tree algorithm, and the K-nearest neighbors' algorithm. The paper also compares the impact of two feature selection methods, Sequential Feature Selection (SFS) and Shapley additive explanations (SHAP) on the accuracies of the classifiers to reduce the number of features needed for classification. The algorithms are accessed based on their accuracy and F1 score. The best performing algorithm is random forest classifier with SFS which achieves accuracy of 0.833 with six features.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| 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 it