Bibliographic record
Abstract
A distributed denial of service (DDoS) attack is widely regarded as a major threat for the current Internet because of its ability to create a huge volume of unwanted traffic. It is hard to detect and respond to DDoS attacks due to large and complex network environments. In this paper, we introduce two distance-based DDoS detection techniques: average distance estimation and distance-based traffic separation. They detect attacks by analyzing distance values and traffic rates. The distance information of a packet can be inferred from the time- to-live (TTL) value of the IP header. In the average distance estimation DDoS detection technique, the prediction of mean distance value is used to define normality. The prediction of traffic arrival rates from different distances is used in the distance-based traffic separation DDoS detection technique. The mean absolute deviation (MAD)-based deviation model provides the legal scope to separate the normality from the abnormality for both the techniques. The results obtained from the NS2-based simulations of the proposed techniques show that the techniques can detect attacks
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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 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".