Taxonomy of Distributed Denial of Service mitigation approaches for cloud computing
Bibliographic record
Abstract
Cloud computing has a central role to play in meeting today׳s business requirements. However, Distributed Denial-of-Service (DDoS) attacks can threaten the availability of cloud functionalities. In recent years, many effort has been expended to detect the various DDoS attack types. In this survey paper, our concentration is on how to mitigate these attacks. We believe that cloud computing technology can substantially change the way we respond to a DDoS attack, based on a number of new characteristics, which were introduced with the advent of this technology. We first present a new taxonomy of DDoS mitigation strategies to organize the work. Then, we go on to discuss the main features of existing DDoS mitigation strategies and explain their functionalities in the cloud environment. Afterwards, we show how the existing DDoS mechanisms fit into the network topology of the cloud. Finally, we discuss some of these DDoS mechanisms in detail, and compare their behavior in the cloud. Our objective is to show how these characteristics bring a novel perspective to existing DDoS mechanisms, and so give researchers new insights into how to mitigate DDoS attacks in the cloud computing.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".