Intelligent Intrusion Detection Based on Multi-Model Federated Learning for Software Defined Network
Bibliographic record
Abstract
To address the challenges posed by traditional network architectures, the Software-Defined Network (SDN) architecture was introduced.However, SDNs are not immune to many security threats (e.g.Dos, Backdoors).In this paper, we present an advanced intrusion detection system that leverages federated learning (FL) and deep learning (DL) techniques to check whether attacks occur or not on SDN.FL has been employed as a collaborative learning technique, enabling various data planes to conduct local training on their respective client datasets.Following local training on each data plane, the local model parameters are securely transmitted to the controller server.At the controller server, these local training parameters are aggregated to construct a global model.The resulting aggregation outcome is then shared back with each local model to update them, enhancing their ability to detect attacks.Three datasets were used to evaluate the efficacy of the suggested method: UNSW-NB15, NF-UQ-NIDS-v2, and CICIDS2017.The obtained results demonstrate a strong performance in anomaly detection, with an accuracy value reach to 95.68%.
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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 | 0.000 |
| 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".