Exploiting Dynamic Platform Protection Technique for Increasing Service MTTF
Bibliographic record
Abstract
Moving Target Defense (MTD) technology protects a target system by complicating the attacking process of adversaries. It has been gaining more and more attention with the massive growth of vulnerabilities and the widespread deployment of critical network services. This paper aims to analyze service Mean Time To Failure (MTTF) in a vulnerable network system which suffers attacks from adversaries. The system consists of multiple Physical Machines (PM) and each PM can support Docker Containers (DC) to run service. It applies Dynamic Platform Protection Technique (DPT), a kind of MTD techniques, to reduce the impact of attacks on service. A DC can be live migrated among these PMs in order to provision continuous service to users. We propose a model which captures the service behaviors during the service execution in the system. Our model allows both service residency/execution time at a PM and service migration time to be generally distributed. We also derive the formula for calculating MTTF and its approximate accuracy is validated through comparing analytical results with simulation results. Moreover, a formula is proposed to predict the total cost of the system, which helps administrators manage the network system effectively.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".