Minimizing Financial Cost of DDoS Attack Defense in Clouds With Fine-Grained Resource Management
Bibliographic record
Abstract
As the cloud systems gain in popularity, they suffer from cyber attacks. One of the most notorious cyber attacks is Distributed Denial of Service (DDoS) attack, which aims to drain the system resources so that the system becomes unresponsive to the genuine users. DDoS attack and defense essentially revolve around resource competition. Many efforts have been made from the perspective of resource investment and management. However, these defending schemes assume that the resources available to defend the attacks are unlimited without taking the financial cost into account. Such coarse-grained defense strategies could cause the problem of resource overprovisioning, which would incur unwanted extra costs to the defender. To tackle this issue, we systematically investigate the problem and propose a birth-death-based fine-grained resource management mechanism, which can both scale in/out and scale down/up. That is, the proposed mechanism adaptively selects the optimal resource leasing mode for cloud service customers so that they can defeat the DDoS attack with minimal financial cost. Extensive analyses and empirical data-based experiments are conducted. The results show both the effectiveness and efficiency of the proposed approach. Comparing to existing work, our proposal can averagely save 53.58% (up to 93.75%) of the cost for the attack defense.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".