Designing Ensemble Deep Learning Intrusion Detection System for DDoS attacks in Software Defined Networks
Bibliographic record
Abstract
Software Defined Networks (SDN) is gaining popularity in academia and the industry. This is due to SDNs ease of programmability, flexibility and centralized management. These networking features allow network administrators and programmers to easily monitor and control the entire network, at a limited cost. However, because of its centralized architecture, the controller becomes a single point of failure. This vulnerability makes it a target to cyber-attacks, but more specifically to Distributed Denial of Service (DDoS) attacks. The DDoS attack may target the SDN network controller in order to disrupt the entire network, causing network resources unavailable to legitimate users. Hence, in this work, we propose an ensemble Deep Learning (DL) Intrusion Detection System (IDS) to detect DDoS attack traffic in SDNs. Our proposed approach build an ensemble of Convolutional Neural Network (CNN), Deep Neural Network (DNN) and Recurrent Neural Network (RNN) model. To train the model, we use feature selection techniques from the literature and utilized the Canadian Institute for Cybersecurity Intrusion Detection System (CIC-IDS2017) as the evaluation dataset. The performance of the proposed model is compared with existing models, and from the results, it is observed that our proposed ensemble deep learning model performs better than ensemble CNN, ensemble RNN and ensemble voting.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".