Cloud Firewall Under Bursty and Correlated Data Traffic: A Theoretical Analysis
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
Cloud firewalls stand as one of the major building blocks of the cloud security framework protecting the Virtual Private Infrastructure against attacks such as the Distributed Denial of Service (DDoS). In order to fully characterize the cloud firewall operation and gain actionable insights on the design of cloud security, performance models for the cloud firewall become imperative. In this article, we propose a multi-dimensional Continuous-Time Markov Chain model for the cloud firewall that takes into account the burstiness and correlation features of the legitimate and malicious data traffic. By adopting the Markov-Modulated Poisson process (MMPP) and the Interrupted Poisson Process (IPP), we identify the workload conditions under which the cloud firewall might be subject to a loss of availability. Furthermore, by comparing the IPP and Poisson attacks, we numerically verify that the cloud firewall is inherently vulnerable to a burstiness-aware attack which might seriously compromise its operation. Additionally, we characterize the joint harmful impact of burstiness and correlation on the cloud firewall that might lead to performance degradation. Finally, we design an elastic cloud firewall by proposing a MMPP-driven load balancing procedure that provisions virtual firewalls dynamically while fulfilling a Service Level Agreement (SLA) latency specification.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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