On the inference and prediction of DDoS campaigns
Bibliographic record
Abstract
Abstract This work proposes a distributed denial‐of‐service (DDoS) inference and forecasting model that aims at providing insights to organizations, security operators, and emergency response teams during and after a DDoS attack. Specifically, our work strives to predict, within minutes, the attacks' features, namely intensity/rate (packets/second) and size (estimated number of used compromised machines/bots). The goal is to understand the future short‐term trend of the ongoing DDoS attack in terms of those features and thus provide the capability to recognize the current as well as future similar situations and hence appropriately respond to the threat. Further, our work aims at investigating DDoS campaigns by proposing a clustering approach to infer various victims targeted by the same campaign and predicting related features. Our analysis employs real darknet data to explore the feasibility of applying the inference and forecasting models on DDoS attacks and evaluate the accuracy of the predictions. To achieve our goal, our proposed approach leverages a number of time series and fluctuation analysis techniques, statistical methods, and forecasting approaches. The extracted inferences from various DDoS case studies exhibit a promising accuracy reaching at some points less than 1% error rate. Further, our approach could lead to a better understanding of the scale, speed, and size of DDoS attacks and generates inferences that could be adopted for immediate response and mitigation. Moreover, the accumulated insights could be used for the purpose of long‐term large‐scale DDoS analysis. Copyright © 2014 John Wiley & Sons, Ltd.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".