Dynamic Defense Framework: A Unified Approach for Intrusion Detection and Mitigation in SDN
Bibliographic record
Abstract
Various challenges have hindered achieving strong cybersecurity within the dynamic network configurations of Software Defined Networking (SDN). Traditional cybersecurity measures, especially in programmable and dynamic network infrastructures like SDNs, are not sufficient in mitigating cyber threats. The proposed Dynamic Defense method includes preprocessing of data, extracting features, filtering features, detecting the attacks and mitigating them. Furthermore, a novel hybrid Coot-Lyrebird optimization algorithm is developed to specifically choose the most impactful features. The selected features are given to the proposed hybrid network that combines Convolutional Neural Network (CNN), SE-ResNeXt, and Long Short-Term Memory (LSTM) networks. Finally, the proposed Deep Q-Network (DQN) model performs attack mitigation measures. The results indicate that the proposed Dynamic Defense has accuracy of 0.999571%.
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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".