On the inference and prediction of DDoS campaigns
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
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.
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 | 0.000 | 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 | 0.001 | 0.001 |
| 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