Exploring Factors Influencing System Administrators' Security Vulnerability Remediation Decisions
Bibliographic record
Abstract
This thesis explores factors influencing system administrators' security vulnerability remediation decisions.Little is known about how system administrators determine when and whether to address security vulnerabilities and how they prioritize their remediation decisions.Herein, we present a preliminary semi-structured interview study with seven system administrators and a large-scale survey study with 124 system administrators working in North America.The findings reveal that factors such as vulnerability severity, administrator's skills and experience, fix complexity, and potential impact on the system greatly influence system administrators' decisions.We also explore the idea of "vulnerability ownership," the system administrator responsible for introducing the vulnerability to the system.We found that the concept of ownership differs based on the type of vulnerability.Our results highlight the importance of system administrators collaborating with their colleagues and external vendors.First and foremost, I would like to express my deepest gratitude to my supervisor, Professor Hala Assal, for her invaluable guidance, expertise, and unwavering support throughout my Master's journey.As an international student, I am incredibly fortunate to have had Professor Assal's patience and kindness, which helped me navigate through academic life and feel welcome in my new
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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 | 0.012 | 0.066 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".