How existing machine learning models for DDoS detection differ in performance and accuracy when applied to synthetic versus real-world network traffic datasets
Bibliographic record
Abstract
Machine learning–based DDoS detection systems frequently report exceptionally high performance, often exceeding 98–99% accuracy. However, such results are predominantly derived from synthetic, laboratory-generated datasets that fail to capture the complexity, variability, and noise of real operational environments. This phenomenon is not unique to cybersecurity; similar patterns have been observed in applied health technologies such as remote blood pressure monitoring, where machine learning models trained on controlled clinical datasets often demonstrate inflated performance but struggle to generalize to real-world home monitoring conditions. This paper empirically demonstrates how multiple machine learning models achieve near-perfect performance when evaluated on controlled, laboratory-created DDoS datasets. Using two widely adopted benchmark datasets, the evaluated models achieved accuracies close to 99%. However, when the same learning methods were applied to a real-world dataset constructed from 28 months of unsolicited network traffic, model accuracy declined to approximately 92%.
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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 | 0.002 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 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".