Next Generation Application-Layer DDoS Defences: Applying the Concepts of Outlier Detection in Data Streams with Concept Drift
Bibliographic record
Abstract
The existing state-of-the art in the field of application-layer DDoS protection is generally designed, and thus effective, only for static Web-domains. To the best of our knowledge, this paper is the first one to study the problem of application-layer DDoS defense in Web-sites of dynamic content and/or organization and under non-trivial bot (i.e., Attack) behavior. The main contributions of the paper are threefold: 1) we provide a detailed taxonomy of the existing and next-generation application-layer HTTP-based DDoS attacks, 2) we discuss the relevance of a branch of data mining theory -- known as data streams with concept drift -- to the problem of application-layer DDoS defense in dynamic Web-domains, 3) we present the outline of our next-generation anti-DDoS system that is intended for dynamic Web-domains facing different sophisticated variants of application-layer DDoS attacks. The paper also includes some of our preliminary experimental results concerning the detection of malicious Web-users/sessions using the proposed system.
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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 | 0.008 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".