Bibliographic record
Abstract
Improving system security during the design phase is challenging but can be costeffective in the long run.Security metrics are a way to measure and manage a system's ability to minimize possible attack opportunities.While several designlevel security metrics exist to evaluate vulnerabilities in system design, it is unclear which metrics provide a sound scientific basis for their characterization.Lack of security knowledge among average development teams and the lack of tool support are additional challenges.In this work, we present a data-driven approach for the security evaluation of system designs to address the above challenges.The approach aims to incrementally improve system security and decision-making at design time.We integrate the attack surface metric which we found to be sound in our evaluation of widely-used security metrics and leverage external data sources to characterize the structural security posture of software systems.Several tools are developed to automate the approach.This work began with an ambitious vision to help organizations develop secure systems and to provide tools that they can use to apply our findings.This work would not have been possible in such a short time frame without the unwavering support, encouragement, and guidance from my supervisor Professor Jason Jaskolka.I also express my deepest gratitude to Professor George Yee who volunteered to collaborate on this work and whose input and guidance played a pivotal role in the success of this work.I also thank Andrew Pullin who helped us make Compass toolkit a reality.I express my sincere gratitude to Professor James Green who played a critical role in advancing my research career.His willingness to enable me to explore and apply my ideas, however ambitious they may be, allowed me to further my learning and impact the community in more ways than one.I
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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 | 0.015 | 0.050 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".