Hybrid Ensemble Learning Framework for Real-Time DDoS Detection and Mitigation in SDN Environments
Bibliographic record
Abstract
Distributed Denial-of-Service (DDoS) attacks continue to pose a significant and evolving threat to network availability and integrity, particularly within Software-Defined Networking (SDN) environments. Traditional signature-based detection techniques often fall short against complex, multi-vector attacks. This study introduces an intelligent defense framework leveraging ensemble machine learning models to enhance DDoS detection. Two hybrid approaches are proposed: one combining Support Vector Machine (SVM) and XGBoost via soft voting, and another integrating Random Forest (RF) and XGBoost through stacking. Evaluation on the CICDDoS2019 dataset using metrics such as Accuracy, Precision, Recall, F1-score, and AUC demonstrates superior performance of both ensembles over individual models. Notably, the soft voting model shows better generalization, while the stacking model yields higher precision. Real-time validation using Mininet and the OpenDaylight controller confirms the framework’s capacity for autonomous threat response. The findings affirm ensemble learning as a robust solution for adaptive DDoS detection in dynamic SDN-based networks.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".