Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 it