Towards Mitigation of Low and Slow Application DDoS Attacks
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
Distributed Denial of Service attacks are a growing threat to organizations and, as defense mechanisms are becoming more advanced, hackers are aiming at the application layer. For example, application layer Low and Slow Distributed Denial of Service attacks are becoming a serious issue because, due to low resource consumption, they are hard to detect. In this position paper, we propose a reference architecture that mitigates the Low and Slow Distributed Denial of Service attacks by utilizing Software Defined Infrastructure capabilities. We also propose two concrete architectures based on the reference architecture: a Performance Model-Based and Off-The-Shelf Components based architecture, respectively. We introduce the Shark Tank concept, a cluster under detailed monitoring that has full application capabilities and where suspicious requests are redirected for further filtering.
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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 | 0.000 | 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