An Aspect-Oriented Approach for Software Security Hardening: from Design to Implementation
Bibliographic record
Abstract
Security is a very challenging task in software engineering. Enforcing security policies should be taken care of during the early phases of the software development life cycle to prevent security breaches in the final product. Since security is a crosscutting concern that pervades the entire software, integrating security solutions at the software design level may result in scattering and tangling security features throughout the entire design. To address this issue, we propose in this paper an aspect-oriented approach for specifying and enforcing security hardening solutions. This approach provides software designers with UML-based capabilities to perform security hardening in a clear and organized way, at the UML design level, without the need to be security experts. We also present the SHP profile, a UML-based security hardening language to describe and specify security hardening solutions at the UML design level. Finally, we explore the efficiency and the relevance of our approach by applying it to a real world case study and present the experimental results.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".