Mitigating Data Imbalance in DDoS Detection for SDN Through Machine Learning Methods
Bibliographic record
Abstract
With the widespread adoption of Software-Defined Networking (SDN), Distributed Denial of Service (DDoS) attacks pose significant threats to network security. Machine learning-based detection methods suffer from data imbalance, where normal traffic significantly outweighs attack traffic, leading to biased models. This study proposes an optimized Voting Classifier that combines Decision Tree and Random Forest algorithms with resampling techniques to improve minority class detection. Experimental results show that the proposed method achieves 99.99% accuracy, 100% recall, and an AUC-ROC of 100%, outperforming baseline classifiers such as Random Forest and Gradient Boosting. Additionally, we evaluate the model's deployment feasibility in SDN using Mininet and the RYU controller. The findings demonstrate the practicality of integrating machine learning-based DDoS detection in real-world SDN environments.
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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 | 0.001 | 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 | 0.001 |
| 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 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".